Michael Rzanny

Complexity of multitrophic interactions in a grassland ecosystem depends on plant species diversity

1. We studied the theoretical prediction that a loss of plant species richness has a strong impact on community interactions among all trophic levels and tested whether decreased plant species diversity results in a less complex structure and reduced interactions in ecological networks. 2. Using plant species-specific biomass and arthropod abundance data from experimental grassland plots (Jena Experiment), we constructed multitrophic functional group interaction webs to compare communities based on 4 and 16 plant species. 427 insect and spider species were classified into 13 functional groups. These functional groups represent the nodes of ecological networks. Direct and indirect interactions among them were assessed using partial Mantel tests. Interaction web complexity was quantified using three measures of network structure: connectance, interaction diversity and interaction strength. 3. Compared with high plant diversity plots, interaction webs based on low plant diversity plots showed reduced complexity in terms of total connectance, interaction diversity and mean interaction strength. Plant diversity effects obviously cascade up the food web and modify interactions across all trophic levels. The strongest effects occurred in interactions between adjacent trophic levels (i.e. predominantly trophic interactions), while significant interactions among plant and carnivore functional groups, as well as horizontal interactions (i.e. interactions between functional groups of the same trophic level), showed rather inconsistent responses and were generally rarer. 4. Reduced interaction diversity has the potential to decrease and destabilize ecosystem processes. Therefore, we conclude that the loss of basal producer species leads to more simple structured, less and more loosely connected species assemblages, which in turn are very likely to decrease ecosystem functioning, community robustness and tolerance to disturbance. Our results suggest that the functioning of the entire ecological community is critically linked to the diversity of its component plants species.

Plant species classification using flower images—A comparative study of local feature representations

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification.

GC content-independent amino acid patterns in bacteria and archaea.

Every organism can be characterized by the amino acid composition of its proteome. So far it was assumed that these compositions are determined by the GC content of the DNA or, in some cases, by extreme lifestyles, like thermophily or halophily. Here, we focussed our analysis on eight amino acids, each of which is encoded by both, GC and AT rich codons, to identify finer amino acid patterns beyond the GC dominance. We investigated the conceptually translated proteomes of 1029 bacterial and archaeal strains with sequenced genomes for amino acid composition. Using correspondence analysis, we found that phylogenetic groups within bacteria and archaea generally can be discriminated from other groups due to their amino acid composition. In some cases, single organisms, e.g. Treponema pallidum strains or Mycoplasma penetrans, are characterized by extreme amino acid compositions. We assume that our data could provide a basis for a new approach to analyze evolution of bacterial and archaeal groups. Furthermore, for single organisms, the detailed knowledge of the amino acid composition of the entire proteome encoded in the genome could lead to a better understanding, important for pharmaceutical or biotechnological applications. We recommend that information about amino acid compositions should be provided in databases, comparable to the GC content of genomes. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Automated plant species identification—Trends and future directions

Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts.

Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

BackgroundAutomated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way.MethodsIn this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination.ResultsThe most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf’s top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf’s boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost.ConclusionsIn conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this study provides a systematic guideline allowing researchers to spend available acquisition resources wisely while yielding the optimal classification accuracy.

Aquifer community structure in dependence of lithostratigraphy in groundwater reservoirs

Groundwater microbiology with respect to different host rocks offers new possibilities to describe and map the habitat harboring approximately half of Earths’ biomass. The Thuringian Basin (Germany) contains formations of the Permian (Zechstein) and Triassic (Muschelkalk and Buntsandstein) with outcrops and deeper regions at the border and central part. Hydro(geo)chemistry and bacterial community structure of 11 natural springs and 20 groundwater wells were analyzed to define typical patterns for each formation. Widespread were Gammaproteobacteria, while Bacilli were present in all wells. Halotolerant and halophilic taxa were present in Zechstein. The occurrence of specific taxa allowed a clear separation of communities from all three lithostratigraphic groups. These specific taxa could be used to follow fluid movement, e.g., from the underlying Zechstein or from nearby saline reservoirs into Buntsandstein aquifers. Thus, we developed a new tool to identify the lithostratigraphic origin of sources in mixed waters. This was verified with entry of surface water, as species not present in the underground Zechstein environments were isolated from the water samples. Thus, our tool shows a higher resolution as compared to hydrochemistry, which is prone to undergo fast dilution if water mixes with other aquifers. Furthermore, the bacteria well adapted to their respective environment showed geographic clustering allowing to differentiate regional aquifers.