Ceased grazing management changes the ecosystem services of semi-natural grasslands

Understanding how drivers of change affect ecosystem services (ES) is of great importance. Indicators of ES can be developed based on biophysical measures and be used to investigate the service flow from ecosystems to socio-ecological systems. However, the ES concept is multivariate and the use of normalized composite indicators reduces complexity and facilitates communication between science and policy. The aim of this study is to analyze how land use change affects ES and species richness and how the effects are modified by environmental factors by using composite indicators based on biophysical indicators. Using multivariate and regression analyses, we analyze the effect of grazing management abandonment in semi-natural grasslands in Norway on six ES: nutrient cycling, pollination, forage quality, aesthetics and global and regional climate regulation in addition to species richness along soil and climate gradients. Nutrient cycling, forage quality, regional climate regulation, aesthetics and species richness are larger in managed compared to abandoned grasslands. There are trade-offs among ES as different management strategies provide various ES and these trade-offs vary along environmental gradients. Management policies that aim to conserve ES need to have conservation goals that are context dependent, should recognize ES trade-offs and be adapted to local conditions

Sources of uncertainty in modeled land carbon storage within and across three MIPs: Diagnosis with three new techniques

This is the final version. Available from the American Meteorological Society via the DOI in this recordTerrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land-Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.This paper is financially supported by the Research and Development Special Fund for Public Welfare Industry of the Ministry of Water Research in China (201501028). JBF and CRS were supported in part by NASA’s Carbon Cycle Science program. JBF was also supported in part by NASA’s Terrestrial Ecology and Carbon Monitoring System programs. JT acknowledges RCN funded project EVA (229771) and BCCR-BIGCHANGE

Sources of uncertainty in modeled land carbon storage within and across three MIPs: Diagnosis with three new techniques

This is the final version. Available from the American Meteorological Society via the DOI in this recordTerrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land-Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.This paper is financially supported by the Research and Development Special Fund for Public Welfare Industry of the Ministry of Water Research in China (201501028). JBF and CRS were supported in part by NASA’s Carbon Cycle Science program. JBF was also supported in part by NASA’s Terrestrial Ecology and Carbon Monitoring System programs. JT acknowledges RCN funded project EVA (229771) and BCCR-BIGCHANGE

When Is a Principal Charged With an Agent’s Knowledge?

Question: Detecting species presence in vegetation and making visual assessment of abundances involve a certain amount of skill, and therefore subjectivity. We evaluated the magnitude of the error in data, and its consequences for evaluating temporal trends. Location: Swedish forest vegetation. Methods: Vegetation data were collected independently by two observers in 342 permanent 100-m2 plots in mature boreal forests. Each plot was visited by one observer from a group of 36 and one of two quality assessment observers. The cover class of 29 taxa was recorded, and presence/absence for an additional 50. Results: Overall, one third of each occurrence was missed by one of the two observers, but with large differences among species. There were more missed occurrences at low abundances. Species occurring at low abundance when present tended to be frequently overlooked. Variance component analyses indicated that cover data on 5 of 17 species had a significant observer bias. Observer-explained variance was < 10% in 15 of 17 species. Conclusion: The substantial number of missed occurrences suggests poor power in detecting changes based on presence/absence data. The magnitude of observer bias in cover estimates was relatively small, compared with random error, and therefore potentially analytically tractable. Data in this monitoring system could be improved by a more structured working model during field work.Original publication: Milberg, P., Bergstedt, J., Fridman, J., Odell, G & Westerberg, L., Systematic and random variation in vegetation monitoring data, 2008, Journal of Vegetation Science, (19), 633-644. http://dx.doi.org/10.3170/2008-8-18423. Copyright: Opulus Press, http://www.opuluspress.se/index.ph

Application of affymetrix array and massively parallel signature sequencing for identification of genes involved in prostate cancer progression

BACKGROUND: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. METHODS: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. RESULTS: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. CONCLUSION: Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases

Deliverable 3.6 zoning plan of case studies : evaluation of spatial management options for the case studies

Within MESMA, nine case studies (CS) represent discrete marine European spatial entities, at different spatial scales, where a spatial marine management framework is in place, under development or considered. These CS (described in more details below) are chosen in such a way (MESMA D. 3.1 ) that they encompass the complexity of accommodating the various user functions of the marine landscape in various regions of the European marine waters. While human activities at sea are competing for space, there is also growing awareness of the possible negative effects of these human activities on the marine ecosystem. As such, system specific management options are required, satisfying current and future sectoral needs, while safeguarding the marine ecosystem from further detoriation. This integrated management approach is embedded in the concept of ecosystem based management (EBM). The goal of marine EBM is to maintain marine ecosystems in a healthy, productive and resilient condition, making it possible that they sustain human use and provide the goods and services required by society (McLeod et al. 2005). Therefore EBM is an environmental mangagement approach that recognises the interactions within a marine ecosystem, including humans. Hence, EBM does not consider single issues, species or ecosystems good and services in isolation. Operationalisation of EBM can be done through place-based or spatial management approaches (Lackey 1998), such as marine spatial planning (MSP). MSP is a public process of analysing and allocating the spatial and temporal distribution of human activities aiming at achieving ecological, economic and social objectives. These objectives are usually formulated through political processes (Douvere et al. 2007, Douvere 2008). Within MESMA, a spatially managed area (SMA) is then defined as “a geographical area within which marine spatial planning initiatives exist in the real world”. Marine spatial planning initiatives refer to existing management measures actually in place within a defined area, or in any stage of a process of putting management in place, e.g. plans or recommendations for a particular area. Management can include management for marine protection (e.g. in MPAs), or management for sectoral objectives (e.g. building a wind farm to meet renewable energy objectives). Within MESMA, SMAs can have different spatial scales. A SMA can be a small, specific area that is managed/planned to be managed for one specific purpose, but it can also be a larger area within which lots of plans or ‘usage zones’ exist. This definition is different from the definition mentioned in the DoW (page 60). The original definition was adapted during a CS leader workshop (2-4 May 2012 in Gent, Belgium) and formally accepted by the MESMA ExB during the ExB meeting in Cork (29-30 May 2012). MSP should result in a marine spatial management plan that will produce the desired future trough explicit decisions about the location and timing of human activities. Ehler & Douvere (2009) consider this spatial management as a beginning toward the the implementation of desired goals and objectives. They describe the spatial management plan as a comprehensive, strategic document that provides the framework and direction for marine spatial management decisions. The plan should identify when, where and how goals and objectives will be met. Zoning (the development of zoning plans) is often an important management measure to implement spatial management plans. The purpose of a zoning plan (Ehler & Douvere 2009) is: To provide protection for biologically and ecologically important habitats, ecosystems, and ecological processes. To seperate conflicting human activities, or to combine compatible activities. To protect the natural values of the marine management area (in MESMA terminology: the SMA) while allowing reasonable human uses of the area. To allocate areas for reasonable human uses while minimising the effects of these human uses on each other, and nature. To preserve some areas of the SMA in their natural state undisturbed by humans except for scientific and educational purposes.peer-reviewe