AGRICULTURE IN AN ECOSYSTEMS FRAMEWORK

By broadening the definition of an ecosystem to include economic activities, can we better characterize the interactions and relationships among agricultural activities and important indicators of ecological system health? This paper addresses research approaches for assessing the role of agriculture in an ecosystems context. Environmental regulation and resource management policies have heightened the interest in understanding interactions among agricultural activities and the natural resource base, including the impacts of agriculture on environmental quality and the impacts on agriculture of ecosystem restoration efforts. What are the most meaningful indicators of environmental quality? Which agricultural practices and policies should be considered, along with which nonagricultural resource uses? Finally, does the evolving thinking about ecosystems permit us to link agricultural practices and policies more directly and meaningfully to conceptions of sustainability, of both natural and socioeconomic systems? This paper presents a brief synopsis of ecosystem management, drawing from several recent governmental initiatives. It then provides an overview of the economics of ecosystem management from the perspective of the role of agriculture; discusses two specific cases, the Pacific Northwest and South Florida; and concludes with a discussion of promising economic approaches, data needs, and caveats to those engaged in policy analysis involving ecosystem restoration.Environmental Economics and Policy,

Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing

On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity

Environmental factors associated with the prevalence of ESBL/AmpC-producing Escherichia coli in wild boar (Sus scrofa)

Antimicrobial resistances (AMR) in bacteria, such as ESBL/AmpC-producing E. coli, are a burden to human and animal health. This burden is mainly driven by the consumption and release of antimicrobial substances into the environment. The pollution and contamination of habitats by AMR in bacteria and antimicrobial substances can lead to the transmission of bacterial AMR to wildlife. Therefore, it is necessary to understand the transmission cycle of antibiotics and resistant bacteria between humans, and animals as well as their occurrences in the environment. Environmental factors associated with the occurrence of bacterial AMR in wildlife can lead to a better understanding of the distribution of bacterial AMR in humans and animals using One Health approaches. Here, we analyzed data gathered in the framework of the German zoonoses monitoring program in 2016 and 2020 using spatiotemporal statistics to identify relevant environmental factors (e.g., livestock density, climatic variables, and human density) in association with the spatial distribution of ESBL/AmpC-producing E. coli. For this purpose, we developed a generic data integration and analysis pipeline to link spatially explicit environmental factors to the monitoring data. Finally, we built a binomial generalized linear mixed model (GLMM) to determine the factors associated with the spatial distribution of ESBL/AmpC-producing E. coli. In 2016 and 2020, 807 fecal samples from hunted wild boar (Sus scrofa L.) were randomly taken in 13 federal states and selectively analyzed for ESBL/AmpC-producing E. coli. Forty-eight isolates were identified in 12 German federal states, with an overall prevalence of 6%. We observed an almost three times higher probability of ESBL/AmpC-producing E. coli isolates in wild boar in counties with high cattle densities (OR = 2.57, p ≤ 0.01). Furthermore, we identified a seasonal effect in areas with high precipitation during the off-hunting seasons (OR = 2.78, p = 0.025) and low precipitation throughout the years (OR = 0.42, p = 0.025). However, due to the low amount of identified isolates, confidence intervals were wide, indicating a high level of uncertainty. This suggests that further studies on smaller scales need to be conducted with multiannual data and improved metadata, e.g., on the location, the hunting procedure, and species characteristics to be collected during field sampling

Constraining the dense matter equation-of-state with radio pulsars

Radio pulsars provide some of the most important constraints for our understanding of matter at supranuclear densities. So far, these constraints are mostly given by precision mass measurements of neutron stars (NS). By combining single measurements of the two most massive pulsars, J0348$+$0432 and J0740$+$6620, the resulting lower limit of 1.98 $M_\odot$ (99% confidence) of the maximum NS mass, excludes a large number of equations of state (EOSs). Further EOS constraints, complementary to other methods, are likely to come from the measurement of the moment of inertia (MOI) of binary pulsars in relativistic orbits. The Double Pulsar, PSR J0737$-$3039A/B, is the most promising system for the first measurement of the MOI via pulsar timing. Reviewing this method, based in particular on the first MeerKAT observations of the Double Pulsar, we provide well-founded projections into the future by simulating timing observations with MeerKAT and the SKA. For the first time, we account for the spin-down mass loss in the analysis. Our results suggest that an MOI measurement with 11% accuracy (68% confidence) is possible by 2030. If by 2030 the EOS is sufficiently well known, however, we find that the Double Pulsar will allow for a 7% test of Lense-Thirring precession, or alternatively provide a $\sim3\sigma$-measurement of the next-to-leading order gravitational wave damping in GR. Finally, we demonstrate that potential new discoveries of double NS systems with orbital periods shorter than that of the Double Pulsar promise significant improvements in these measurements and the constraints on NS matter.Comment: 13 pages, 8 figures. Accepted by MNRA

Evolution of economic entities under heterogeneous political/environmental conditions within a Bak-Sneppen-like dynamics

A model for economic behavior, under heterogeneous spatial economic conditions is developed. The role of selection pressure in a Bak-Sneppen-like dynamics with entity diffusion on a lattice is studied by Monte-Carlo simulation taking into account business rule(s), like enterprise - enterprise short range location "interaction"(s), business plan(s) through spin-offs or merging and enterprise survival evolution law(s). It is numerically found that the model leads to a sort of phase transition for the fitness gap as a function of the selection pressure.Comment: 6 figures. to be published in Physica

Antarctic sympagic meiofauna in winter: Comparing diversity, abundance and biomass between perennially and seasonally ice-covered regions

This study of Antarctic sympagic meiofauna in pack ice during late winter compares communities between the perennially ice-covered western Weddell Sea and the seasonally ice-covered southern Indian Ocean. Sympagic meiofauna (proto- and metazoans >20 μm) and eggs >20 μm were studied in terms of diversity, abundance and carbon biomass, and with respect to vertical distribution. Metazoan meiofauna had significantly higher abundance and biomass in the western Weddell Sea (medians: 31.1×103 m−2 and 6.53mg m−2, respectively) than in the southern Indian Ocean (medians: 1.0×10 103 m−2and 0.06 mg m−2, respectively). Metazoan diversity was also significantly higher in the western Weddell Sea. Furthermore, the two regions differed significantly in terms of meiofauna community composition, as revealed through multivariate analyses. The overall diversity of sympagic meiofauna was high, and integrated abundance and biomass of total meiofauna were also high in both regions (0.6–178.6×103 m−2 and 0.02–89.70mg m−2, respectively), mostly exceeding values reported earlier from the western Weddell Sea in winter. We attribute the differences in meiofauna communities between the two regions to the older first-year ice and multi-year ice that is present in the western Weddell Sea, but not in the southern Indian Ocean. Our study indicates the significance of perennially ice-covered regions for the establishment of diverse and abundant meiofauna communities. Furthermore, it highlights the potential importance of sympagic meiofauna for the organic matter pool and trophic interactions in sea ice

Recognition of manual actions using vector quantization and dynamic time warping

Abstract. The recognition of manual actions, i.e., hand movements, hand postures and gestures, plays an important role in human-computer interaction, while belonging to a category of particularly difficult tasks. Using a Vicon system to capture 3D spatial data, we investigate the recognition of manual actions in tasks such as pouring a cup of milk and writing into a book. We propose recognizing sequences in multidimensional time-series by first learning a smooth quantization of the data, and then using a variant of dynamic time warping to recognize short sequences of prototypical motions in a long unknown sequence. An experimental analysis validates our approach. Short manual actions are successfully recognized and the approach is shown to be spatially invariant. We also show that the approach speeds up processing while not decreasing recognition performance

Large-Scale Electron Microscopy Maps of Patient Skin and Mucosa Provide Insight into Pathogenesis of Blistering Diseases

Large-scale electron microscopy (“nanotomy”) allows straight forward ultrastructural examination of tissue, cells, organelles, and macromolecules in a single data set. Such data set equals thousands of conventional electron microscopy images and is freely accessible (www.nanotomy.org). The software allows zooming in and out of the image from total overview to nanometer scale resolution in a ‘Google Earth’ approach. We studied the life-threatening human autoimmune blistering disease pemphigus, using nanotomy. The pathomechanism of cell–cell separation (acantholysis) that underlies the blistering is poorly understood. Ultrastructural examination of pemphigus tissue revealed previously unreported findings: (i) the presence of double-membrane structures between cells in all pemphigus types; (ii) the absence of desmosomes around spontaneous blisters in pemphigus foliaceus (PF); (iii) lower level blistering in PF when force induced; and (iv) intercellular widening at non-acantholytic cell layers. Thus, nanotomy delivers open-source electron microscopic maps of patient tissue, which can be analyzed for additional anomalies from any computer by experts from different fields