A calculus for distrust and mistrust

Properties of trust are becoming widely studied in several applications within the computational domain. On the contrary, negative trust attribution is less well-defined and related issues are yet to be approached and resolved. We present a natural deduction calculus for trust protocols and its negative forms, distrust and mistrust. The calculus deals efficiently with forms of trust transitivity and negative trust multiplication and we briefly illustrate some possible applications

Climate and species affect fine root production with long-term fertilization in acidic tussock tundra near Toolik Lake, Alaska

Author Posting. © The Author(s), 2007. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Oecologia 153 (2007): 643-652, doi:10.1007/s00442-007-0753-8.Long-term fertilization of acidic tussock tundra has led to changes in plant species composition, increases in aboveground production and biomass and substantial losses of soil organic carbon (SOC). Root litter is an important input to SOC pools, though little is known about fine root demography in tussock tundra. In this study, we examined the response of fine root production and live standing fine root biomass to short- and long-term fertilization, as changes in fine root demography may contribute to observed declines in SOC. Live standing fine root biomass increased with long-term fertilization, while fine root production declined, reflecting replacement of the annual fine root system of Eriophorum vaginatum, with the long-lived fine roots of Betula nana. Fine root production increased in fertilized plots during an unusually warm growing season, but remained unchanged in control plots, consistent with observations that B. nana shows a positive response to climate warming. Calculations based on a few simple assumptions suggest changes in fine root demography with long-term fertilization and species replacement could account for between 20 and 39% of observed declines in SOC stocks.This project was supported by National Science Foundation research grants 9810222, 9911681, 0221606 and 0528748

Study of Zγ events and limits on anomalous ZZγ and Zγγ couplings in pp̄ collisions at s=1.96TeV

We present a measurement of the Zγ production cross section and limits on anomalous ZZγ and Zγγ couplings for form-factor scales of Λ=750 and 1000 GeV. The measurement is based on 138 (152) candidates in the eeγ (μμγ) final state using 320(290)pb-1 of pp̄ collisions at s=1.96TeV. The 95% C.L. limits on real and imaginary parts of individual anomalous couplings are |h10,30Z|<0.23, |h20,40Z|<0.020, |h10,30γ|<0.23, and |h20,40γ|<0.019 for Λ=1000GeV. © 2005 The American Physical Society

Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes

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

Vegetation Type Dominates the Spatial Variability in CH<inf>4</inf> Emissions Across Multiple Arctic Tundra Landscapes

Methane (CH4) emissions from Arctic tundra are an important feedback to global climate. Currently, modelling and predicting CH4 fluxes at broader scales are limited by the challenge of upscaling plot-scale measurements in spatially heterogeneous landscapes, and by uncertainties regarding key controls of CH4 emissions. In this study, CH4 and CO2 fluxes were measured together with a range of environmental variables and detailed vegetation analysis at four sites spanning 300 km latitude from Barrow to Ivotuk (Alaska). We used multiple regression modelling to identify drivers of CH4 flux, and to examine relationships between gross primary productivity (GPP), dissolved organic carbon (DOC) and CH4 fluxes. We found that a highly simplified vegetation classification consisting of just three vegetation types (wet sedge, tussock sedge and other) explained 54% of the variation in CH4 fluxes across the entire transect, performing almost as well as a more complex model including water table, sedge height and soil moisture (explaining 58% of the variation in CH4 fluxes). Substantial CH4 emissions were recorded from tussock sedges in locations even when the water table was lower than 40 cm below the surface, demonstrating the importance of plant-mediated transport. We also found no relationship between instantaneous GPP and CH4 fluxes, suggesting that models should be cautious in assuming a direct relationship between primary production and CH4 emissions. Our findings demonstrate the importance of vegetation as an integrator of processes controlling CH4 emissions in Arctic ecosystems, and provide a simplified framework for upscaling plot scale CH4 flux measurements from Arctic ecosystems

Reconciling carbon-cycle concepts, terminology, and methods

Author Posting. © The Author(s), 2006. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecosystems 9 (2006): 1041-1050, doi:10.1007/s10021-005-0105-7.Recent patterns and projections of climatic change have focused increased scientific and public attention on patterns of carbon (C) cycling and its controls, particularly the factors that determine whether an ecosystem is a net source or sink of atmospheric CO2. Net ecosystem production (NEP), a central concept in C-cycling research, has been used to represent two different concepts by C-cycling scientists. We propose that NEP be restricted to just one of its two original definitions—the imbalance between gross primary production (GPP) and ecosystem respiration (ER), and that a new term—net ecosystem carbon balance (NECB)—be applied to the net rate of C accumulation in (or loss from; negative sign) ecosystems. NECB differs from NEP when C fluxes other than C fixation and respiration occur or when inorganic C enters or leaves in dissolved form. These fluxes include leaching loss or lateral transfer of C from the ecosystem; emission of volatile organic C, methane, and carbon monoxide; and soot and CO2 from fire. C fluxes in addition to NEP are particularly important determinants of NECB over long time scales. However, even over short time scales, they are important in ecosystems such as streams, estuaries, wetlands, and cities. Recent technological advances have led to a diversity of approaches to measuring C fluxes at different temporal and spatial scales. These approaches frequently capture different components of NEP or NECB and can therefore be compared across scales only by carefully specifying the fluxes included in the measurements. By explicitly identifying the fluxes that comprise NECB and other components of the C cycle, such as net ecosystem exchange (NEE) and net biome production (NBP), we provide a less ambiguous framework for understanding and communicating recent changes in the global C cycle. Key words: Net ecosystem production, net ecosystem carbon balance, gross primary production, ecosystem respiration, autotrophic respiration, heterotrophic respiration, net ecosystem exchange, net biome production, net primary production