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Understanding and predicting the composition and spatial structure of communities is a central challenge in ecology. An important structural property of animal communities is the distribution of individual home ranges. Home range formation is controlled by resource heterogeneity, the physiology and behaviour of individual animals, and their intra- and interspecific interactions. However, a quantitative mechanistic understanding of how home range formation influences community composition is still lacking. To explore the link between home range formation and community composition in heterogeneous landscapes we combine allometric relationships for physiological properties with an algorithm that selects optimal home ranges given locomotion costs, resource depletion and competition in a spatially-explicit individual-based modelling framework. From a spatial distribution of resources and an input distribution of animal body mass, our model predicts the size and location of individual home ranges as well as the individual size distribution (ISD) in an animal community. For a broad range of body mass input distributions, including empirical body mass distributions of North American and Australian mammals, our model predictions agree with independent data on the body mass scaling of home range size and individual abundance in terrestrial mammals. Model predictions are also robust against variation in habitat productivity and landscape heterogeneity. The combination of allometric relationships for locomotion costs and resource needs with resource competition in an optimal foraging framework enables us to scale from individual properties to the structure of animal communities in heterogeneous landscapes. The proposed spatially-explicit modelling concept not only allows for detailed investigation of landscape effects on animal communities, but also provides novel insights into the mechanisms by which resource competition in space shapes animal communities.
Standing stocks are typically easier to measure than process rates such as production. Hence, stocks are often used as indicators of ecosystem functions although the latter are generally more strongly related to rates than to stocks. The regulation of stocks and rates and thus their variability over time may differ, as stocks constitute the net result of production and losses. Based on long-term high frequency measurements in a large, deep lake we explore the variability patterns in primary and bacterial production and relate them to those of the corresponding standing stocks, i.e. chlorophyll concentration, phytoplankton and bacterial biomass. We employ different methods (coefficient of variation, spline fitting and spectral analysis) which complement each other for assessing the variability present in the plankton data, at different temporal scales. In phytoplankton, we found that the overall variability of primary production is dominated by fluctuations at low frequencies, such as the annual, whereas in stocks and chlorophyll in particular, higher frequencies contribute substantially to the overall variance. This suggests that using standing stocks instead of rate measures leads to an under- or overestimation of food shortage for consumers during distinct periods of the year. The range of annual variation in bacterial production is 8 times greater than biomass, showing that the variability of bacterial activity (e.g. oxygen consumption, remineralisation) would be underestimated if biomass is used. The P/B ratios were variable and although clear trends are present in both bacteria and phytoplankton, no systematic relationship between stock and rate measures were found for the two groups. Hence, standing stock and process rate measures exhibit different variability patterns and care is needed when interpreting the mechanisms and implications of the variability encountered.
The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks - a recently developed approach - are promising candidates for characterising the system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. In a first order approximation, the results of recurrence network analysis are invariant to changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. This suggests its application as a general exploratory tool of time series analysis complementing existing methods.
The aim of this paper is to estimate the Hurst parameter of Fractional Gaussian Noise (FGN) using Bayesian inference. We propose an estimation technique that takes into account the full correlation structure of this process. Instead of using the integrated time series and then applying an estimator for its Hurst exponent, we propose to use the noise signal directly. As an application we analyze the time series of the Nile River, where we find a posterior distribution which is compatible with previous findings. In addition, our technique provides natural error bars for the Hurst exponent.
To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes.
The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times.
Using the system, a single researcher can phenotype up to 7000 plants d(-1). Technical variance is very low (typically < 2%). We show quantitative results for the growth-impaired starch-excessmutant sex4-3 and the growth-enhancedmutant grf9.
We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions.
Despite the importance of rhizosphere properties for water flow from soil to roots, there is limited quantitative information on the distribution of water in the rhizosphere of plants.
Here, we used neutron tomography to quantify and visualize the water content in the rhizosphere of the plant species chickpea (Cicer arietinum), white lupin (Lupinus albus), and maize (Zea mays) 12 d after planting.
We clearly observed increasing soil water contents (h) towards the root surface for all three plant species, as opposed to the usual assumption of decreasing water content. This was true for tap roots and lateral roots of both upper and lower parts of the root system. Furthermore, water gradients around the lower part of the roots were smaller and extended further into bulk soil compared with the upper part, where the gradients in water content were steeper.
Incorporating the hydraulic conductivity and water retention parameters of the rhizosphere into our model, we could simulate the gradual changes of h towards the root surface, in agreement with the observations. The modelling result suggests that roots in their rhizosphere may modify the hydraulic properties of soil in a way that improves uptake under dry conditions.
Genetic differentiation in the competitive and reproductive ability of invading populations can result from genetic Allee effects or r/K selection at the local or range-wide scale. However, the neutral relatedness of populations may either mask or falsely suggest adaptation and genetic Allee effects.
In a common-garden experiment, we investigated the competitive and reproductive ability of invasive Senecio inaequidens populations that vary in neutral genetic diversity, population age and field vegetation cover. To account for population relatedness, we analysed the experimental results with 'animal models' adopted from quantitative genetics.
Consistent with adaptive r/K differentiation at local scales, we found that genotypes from low-competition environments invest more in reproduction and are more sensitive to competition. By contrast, apparent effects of large-scale r/K differentiation and apparent genetic Allee effects can largely be explained by neutral population relatedness.
Invading populations should not be treated as homogeneous groups, as they may adapt quickly to small-scale environmental variation in the invaded range. Furthermore, neutral population differentiation may strongly influence invasion dynamics and should be accounted for in analyses of common-garden experiments.
Biomimetic synthesis of chiral erbium-doped silver/peptide/silica core-shell nanoparticles (ESPN)
(2011)
Peptide-modified silver nanoparticles have been coated with an erbium-doped silica layer using a method inspired by silica biomineralization. Electron microscopy and small-angle X-ray scattering confirm the presence of an Ag/peptide core and silica shell. The erbium is present as small Er(2)O(3) particles in and on the silica shell. Raman, IR, UV-Vis, and circular dichroism spectroscopies show that the peptide is still present after shell formation and the nanoparticles conserve a chiral plasmon resonance. Magnetic measurements find a paramagnetic behavior. In vitro tests using a macrophage cell line model show that the resulting multicomponent nanoparticles have a low toxicity for macrophages, even on partial dissolution of the silica shell.
We study pattern-forming instabilities in reaction-advection-diffusion systems. We develop an approach based on Lyapunov-Bloch exponents to figure out the impact of a spatially periodic mixing flow on the stability of a spatially homogeneous state. We deal with the flows periodic in space that may have arbitrary time dependence. We propose a discrete in time model, where reaction, advection, and diffusion act as successive operators, and show that a mixing advection can lead to a pattern-forming instability in a two-component system where only one of the species is advected. Physically, this can be explained as crossing a threshold of Turing instability due to effective increase of one of the diffusion constants.
Ionic liquid Crystals constitute highly versatile materials that have drawn much interest these past few years in the fields of academic research and industrial development. In this respect, the present article is intended as an update of K. Binnemans review published in 2005, but focusing exclusively on the imidazolium cation - the most widely studied. Herein, imidazolium-containing thermotropic liquid crystalline materials will be sorted by molecular structure (mono-, bis-, poly-imidazolium compounds, with symmetrical and non-symmetrical structures) and discussed. Their physico-chemical properties will be exposed in order to adduce the relevancy and potential of the imidazolium platform in various fields of research.