Several longer-term assembly studies on ex-arable land have found that species that arrive first at a disturbed site can play a key role in the further development of the community and that this ...priority effect influences aboveground productivity, species diversity and stability of the grassland communities that develop. Restoration of nutrient poor, species rich grasslands is often limited by seed dispersal as well as the accessibility of suitable microsites for establishment. Sowing species (i.e. creating priority effects for further assembly) may help overcome such dispersal barriers, but the potential of using priority effects for restoration has not been tested in this type of dry grassland. We tested the hypothesis that sowing two different seed mixtures used for dry acidic grassland restoration onto a sandy substrate (which formed an equivalent to a primary succession) would create priority effects, and that these priority effects would be sustained over a number of years. We followed community assembly and measured aboveground productivity for four years after sowing. We found that priority effects caused by sowing of differently diverse mixtures did also occur in dry acidic grassland habitat, but that how persistent they were over time depended on the response variable considered. Priority effects on species number were not as strong as found in previous ex-arable land studies, whereas priority effects for aboveground productivity were still visible after 4 years. In addition, functional composition of the community still reflected the composition of the seed mixtures 4 years later. Our results suggest that priority effects can occur in nutrient-poor dry acidic grassland but in contrast to more nutrient-rich sites the breadth of responses affected may not be as wide.
•Possible priority effects of sowing diverse mixtures tested in dry acidic grassland.•Priority effects found even four years after sowing but effects varied depending on parameter measured.•Effects found were not as clear as in other studies in more mesic grassland.•Lower establishment of target species (microsite limitation) in line with other dry grassland studies.
Dicot leaves grow with pronounced diel (24-h) cycles that are controlled by a complex network of factors. It is an open question to what extent leaf growth dynamics are controlled by long-range or by ...local signals. To address this question, we established a stereoscopic imaging system, GROWSCREEN 3D, which quantifies surface growth of isolated leaf discs floating on nutrient solution in wells of microtiter plates. A total of 458 leaf discs of tobacco (Nicotiana tabacum) were cut at different developmental stages, incubated, and analyzed for their relative growth rates. The camera system was automatically displaced across the array of leaf discs; visualization and camera displacement took about 12 s for each leaf disc, resulting in a time interval of 1.5 h for consecutive size analyses. Leaf discs showed a comparable diel leaf growth cycle as intact leaves but weaker peak growth activity. Hence, it can be concluded that the timing of leaf growth is regulated by local rather than by systemic control processes. This conclusion was supported by results from leaf discs of Arabidopsis (Arabidopsis thaliana) Landsberg erecta wild-type plants and starch-freel mutants. At night, utilization of transitory starch leads to increased growth of Landsberg erecta wild-type discs compared with starch-freel discs. Moreover, the decrease of leaf disc growth when exposed to different concentrations of glyphosate showed an immediate dose-dependent response. Our results demonstrate that a dynamic leaf disc growth analysis as we present it here is a promising approach to uncover the effects of internal and external cues on dicot leaf development.
Differential growth processes in root and shoot growth zones are governed by the transport kinetics of auxin and other plant hormones. While gene expression and protein localization of hormone ...transport facilitators are currently being unraveled using state-of-the-art techniques of live cell imaging, the quantitative analysis of growth reactions is lagging behind because of a lack of suitable methods. A noninvasive technique, based on digital image sequence processing, for visualizing and quantifying highly resolved spatio-temporal root growth processes was applied in the model plant Arabidopsis thaliana and was adapted to provide precise information on differential curvature production activity within the root growth zone. Comparison of root gravitropic curvature kinetics in wild-type and mutant plants altered in a facilitator for auxin translocation allowed the determination of differences in the location and in the temporal response of curvature along the growth zone between the investigated plant lines. The findings of the quantitative growth analysis performed here confirm the proposed action of the investigated transport facilitator. The procedure developed here for the investigation of differential growth processes is a valuable tool for characterizing the phenomenology of a wide range of shoot and root growth movements and hence facilitates elucidation of their molecular characterization.
In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the ...signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey's biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: it has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space.
We discuss the basic concepts of computer vision with stochastic partial differential equations (SPDEs). In typical approaches based on partial differential equations (PDEs), the end result in the ...best case is usually one value per pixel, the “expected” value. Error estimates or even full probability density functions PDFs are usually not available. This paper provides a framework allowing one to derive such PDFs, rendering computer vision approaches into measurements fulfilling scientific standards due to full error propagation. We identify the image data with random fields in order to model images and image sequences which carry uncertainty in their gray values, e.g. due to noise in the acquisition process.
The noisy behaviors of gray values is modeled as stochastic processes which are approximated with the method of generalized polynomial chaos (Wiener-Askey-Chaos). The Wiener-Askey polynomial chaos is combined with a standard spatial approximation based upon piecewise multi-linear finite elements. We present the basic building blocks needed for computer vision and image processing in this stochastic setting, i.e. we discuss the computation of stochastic moments, projections, gradient magnitudes, edge indicators, structure tensors, etc. Finally we show applications of our framework to derive stochastic analogs of well known PDEs for de-noising and optical flow extraction. These models are discretized with the stochastic Galerkin method. Our selection of SPDE models allows us to draw connections to the classical deterministic models as well as to stochastic image processing not based on PDEs. Several examples guide the reader through the presentation and show the usefulness of the framework.
Standard optical flow methods for motion or disparity estimation use a brightness constancy constraint equation (BCCE). This BCCE either handles a moving camera imaging a non-moving scene or a fixed ...camera imaging a moving scene. In this paper a BCCE is developed that can handle instantaneous motion of the camera on a 2D plane normal to the viewing direction and motion of the imaged scene. From the thus acquired up to 5 dimensional data set 3D object motion, 3D surface element position, and -normals can be estimated simultaneously. Experiments using 1d or 2d camera grids and a weighted total least squares (TLS) estimation scheme demonstrate performance in terms of systematic error and noise stability, and show technical implications.
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf ...segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset aug-mentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DC-GAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128 x 128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance segmentation and ...cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results.
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