The Multilateral Instrument: Avoidance of Permanent Establishment Status and the Reservations on behalf of Australia and the UK

This paper considers fully probabilistic system models. Each transition is quantified with a probability—its likelihood of occurrence. Properties are expressed as automata that either accept or reject system runs. The central question is to determine the fraction of accepted system runs. We also consider probabilistic timed system models. Their properties are timed automata that accept timed runs iff all timing constraints resent in the automaton are met; otherwise it rejects. The central question is to determine the fraction of accepted timed system runs

First circumpolar assessment of Arctic freshwater phytoplankton and zooplankton diversity : Spatial patterns and environmental factors

Arctic freshwaters are facing multiple environmental pressures, including rapid climate change and increasing land-use activities. Freshwater plankton assemblages are expected to reflect the effects of these stressors through shifts in species distributions and changes to biodiversity. These changes may occur rapidly due to the short generation times and high dispersal capabilities of both phyto- and zooplankton. Spatial patterns and contemporary trends in plankton diversity throughout the circumpolar region were assessed using data from more than 300 lakes in the U.S.A. (Alaska), Canada, Greenland, Iceland, the Faroe Islands, Norway, Sweden, Finland, and Russia. The main objectives of this study were: (1) to assess spatial patterns of plankton diversity focusing on pelagic communities; (2) to assess dominant component of beta diversity (turnover or nestedness); (3) to identify which environmental factors best explain diversity; and (4) to provide recommendations for future monitoring and assessment of freshwater plankton communities across the Arctic region. Phytoplankton and crustacean zooplankton diversity varied substantially across the Arctic and was positively related to summer air temperature. However, for zooplankton, the positive correlation between summer temperature and species numbers decreased with increasing latitude. Taxonomic richness was lower in the high Arctic compared to the sub- and low Arctic for zooplankton but this pattern was less clear for phytoplankton. Fennoscandia and inland regions of Russia represented hotspots for, respectively, phytoplankton and zooplankton diversity, whereas isolated regions had lower taxonomic richness. Ecoregions with high alpha diversity generally also had high beta diversity, and turnover was the most important component of beta diversity in all ecoregions. For both phytoplankton and zooplankton, climatic variables were the most important environmental factors influencing diversity patterns, consistent with previous studies that examined shorter temperature gradients. However, barriers to dispersal may have also played a role in limiting diversity on islands. A better understanding of how diversity patterns are determined by colonisation history, environmental variables, and biotic interactions requires more monitoring data with locations dispersed evenly across the circumpolar Arctic. Furthermore, the importance of turnover in regional diversity patterns indicates that more extensive sampling is required to fully characterise the species pool of Arctic lakes.Peer reviewe

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Interpretative and predictive modelling of Joint European Torus collisionality scans

Transport modelling of Joint European Torus (JET) dimensionless collisionality scaling experiments in various operational scenarios is presented. Interpretative simulations at a fixed radial position are combined with predictive JETTO simulations of temperatures and densities, using the TGLF transport model. The model includes electromagnetic effects and collisions as well as □(→┬E ) X □(→┬B ) shear in Miller geometry. Focus is on particle transport and the role of the neutral beam injection (NBI) particle source for the density peaking. The experimental 3-point collisionality scans include L-mode, and H-mode (D and H and higher beta D plasma) plasmas in a total of 12 discharges. Experimental results presented in (Tala et al 2017 44th EPS Conf.) indicate that for the H-mode scans, the NBI particle source plays an important role for the density peaking, whereas for the L-mode scan, the influence of the particle source is small. In general, both the interpretative and predictive transport simulations support the experimental conclusions on the role of the NBI particle source for the 12 JET discharges

First circumpolar assessment of Arctic freshwater phytoplankton and zooplankton diversity: Spatial patterns and environmental factors

1. Arctic freshwaters are facing multiple environmental pressures, including rapid climate change and increasing land-use activities. Freshwater plankton assemblages are expected to reflect the effects of these stressors through shifts in species distributions and changes to biodiversity. These changes may occur rapidly due to the short generation times and high dispersal capabilities of both phyto- and zooplankton. 2. Spatial patterns and contemporary trends in plankton diversity throughout the circumpolar region were assessed using data from more than 300 lakes in the U.S.A. (Alaska), Canada, Greenland, Iceland, the Faroe Islands, Norway, Sweden, Finland, and Russia. The main objectives of this study were: (1) to assess spatial patterns of plankton diversity focusing on pelagic communities; (2) to assess dominant component of β diversity (turnover or nestedness); (3) to identify which environmental factors best explain diversity; and (4) to provide recommendations for future monitoring and assessment of freshwater plankton communities across the Arctic region. 3. Phytoplankton and crustacean zooplankton diversity varied substantially across the Arctic and was positively related to summer air temperature. However, for zooplankton, the positive correlation between summer temperature and species numbers decreased with increasing latitude. Taxonomic richness was lower in the high Arctic compared to the sub- and low Arctic for zooplankton but this pattern was less clear for phytoplankton. Fennoscandia and inland regions of Russia represented hotspots for, respectively, phytoplankton and zooplankton diversity, hereas isolated regions had lower taxonomic richness. Ecoregions with high α diversity generally also had high β diversity, and turnover was the most important component of β diversity in all ecoregions. 4. For both phytoplankton and zooplankton, climatic variables were the most important environmental factors influencing diversity patterns, consistent with previous studies that examined shorter temperature gradients. However, barriers to dispersal may have also played a role in limiting diversity on islands. A better understanding of how diversity patterns are determined by colonisation history, environmental variables, and biotic interactions requires more monitoring data with locations dispersed evenly across the circumpolar Arctic. Furthermore, the importance of turnover in regional diversity patterns indicates that more extensive sampling is required to fully characterise the species pool of Arctic lakes. α diversity, β diversity, ecoregions, latitude, taxonomic richness, temperaturepublishedVersio