INFERRING ROUTING PREFERENCES OF BICYCLISTS FROM SPARSE SETS OF TRAJECTORIES

Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimumweight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment

Density-Independent Mortality and Increasing Plant Diversity Are Associated with Differentiation of Taraxacum officinale into r- and K-Strategists

Background: Differential selection between clones of apomictic species may result in ecological differentiation without mutation and recombination, thus offering a simple system to study adaptation and life-history evolution in plants. Methodology/Principal Findings: We caused density-independent mortality by weeding to colonizer populations of the largely apomictic Taraxacum officinale (Asteraceae) over a 5-year period in a grassland biodiversity experiment (Jena Experiment). We compared the offspring of colonizer populations with resident populations deliberately sown into similar communities. Plants raised from cuttings and seeds of colonizer and resident populations were grown under uniform conditions. Offspring from colonizer populations had higher reproductive output, which was in general agreement with predictions of r-selection theory. Offspring from resident populations had higher root and leaf biomass, fewer flower heads and higher individual seed mass as predicted under K-selection. Plants grown from cuttings and seeds differed to some degree in the strength, but not in the direction, of their response to the r- vs. K-selection regime. More diverse communities appeared to exert stronger K-selection on resident populations in plants grown from cuttings, while we did not find significant effects of increasing species richness on plants grown from seeds. Conclusions/Significance: Differentiation into r- and K-strategists suggests that clones with characteristics of r-strategists were selected in regularly weeded plots through rapid colonization, while increasing plant diversity favoured the selection of clones with characteristics of K-strategists in resident populations. Our results show that different selection pressures may result in a rapid genetic differentiation within a largely apomictic species. Even under the assumption that colonizer and resident populations, respectively, happened to be r- vs. K-selected already at the start of the experiment, our results still indicate that the association of these strategies with the corresponding selection regimes was maintained during the 5-year experimental period

Plant Community Diversity Influences Allocation to Direct Chemical Defence in Plantago lanceolata

Background: Forecasting the consequences of accelerating rates of changes in biodiversity for ecosystem functioning requires a mechanistic understanding of the relationships between the structure of biological communities and variation in plant functional characteristics. So far, experimental data of how plant species diversity influences the investment of individual plants in direct chemical defences against herbivores and pathogens is lacking. Methodology/Principal Findings: We used Plantago lanceolata as a model species in experimental grasslands differing in species richness and composition (Jena Experiment) to investigate foliar concentrations of the iridoid glycosides (IG), catalpol and its biosynthetic precursor aucubin. Total IG and aucubin concentrations decreased, while catalpol concentrations increased with increasing plant diversity in terms of species or functional group richness. Negative plant diversity effects on total IG and aucubin concentrations correlated with increasing specific leaf area of P. lanceolata, suggesting that greater allocation to light acquisition reduced the investment into these carbon-based defence components. In contrast, increasing leaf nitrogen concentrations best explained increasing concentrations of the biosynthetically more advanced IG, catalpol. Observed levels of leaf damage explained a significant proportion of variation in total IG and aucubin concentrations, but did not account for variance in catalpol concentrations. Conclusions/Significance: Our results clearly show that plants growing in communities of varying species richness an

Effect of four plant species on soil 15N-access and herbage yield in temporary agricultural grasslands

Positive plant diversity-productivity relationships have been reported for experimental semi-natural grasslands (Cardinale et al. 2006; Hector et al. 1999; Tilman et al. 1996) as well as temporary agricultural grasslands (Frankow-Lindberg et al. 2009; Kirwan et al. 2007; Nyfeler et al. 2009; Picasso et al. 2008). Generally, these relationships are explained, on the one hand, by niche differentiation and facilitation (Hector et al. 2002; Tilman et al. 2002) and, on the other hand, by greater probability of including a highly productive plant species in high diversity plots (Huston 1997). Both explanations accept that diversity is significant because species differ in characteristics, such as root architecture, nutrient acquisition and water use efficiency, to name a few, resulting in composition and diversity being important for improved productivity and resource use (Naeem et al. 1994; Tilman et al. 2002). Plant diversity is generally low in temporary agricultural grasslands grown for ruminant fodder production. Grass in pure stands is common, but requires high nitrogen (N) inputs. In terms of N input, two-species grass-legume mixtures are more sustainable than grass in pure stands and consequently dominate low N input grasslands (Crews and Peoples 2004; Nyfeler et al. 2009; Nyfeler et al. 2011). In temperate grasslands, N is often the limiting factor for productivity (Whitehead 1995). Plant available soil N is generally concentrated in the upper soil layers, but may leach to deeper layers, especially in grasslands that include legumes (Scherer-Lorenzen et al. 2003) and under conditions with surplus precipitation (Thorup-Kristensen 2006). To improve soil N use efficiency in temporary grasslands, we propose the addition of deep-rooting plant species to a mixture of perennial ryegrass and white clover, which are the most widespread forage plant species in temporary grasslands in a temperate climate (Moore 2003). Perennial ryegrass and white clover possess relatively shallow root systems (Kutschera and Lichtenegger 1982; Kutschera and Lichtenegger 1992) with effective rooting depths of <0.7 m on a silt loamy site (Pollock and Mead 2008). Grassland species, such as lucerne and chicory, grow their tap-roots into deep soil layers and exploit soil nutrients and water in soil layers that the commonly grown shallow-rooting grassland species cannot reach (Braun et al. 2010; Skinner 2008). Chicory grown as a catch crop after barley reduced the inorganic soil N down to 2.5 m depth during the growing season, while perennial ryegrass affected the inorganic soil N only down to 1 m depth (Thorup-Kristensen 2006). Further, on a Wakanui silt loam in New Zealand chicory extracted water down to 1.9 m and lucerne down to 2.3 m soil depth, which resulted in greater herbage yields compared with a perennial ryegrass-white clover mixture, especially for dryland plots (Brown et al. 2005). There is little information on both the ability of deep- and shallow-rooting grassland species to access soil N from different vertical soil layers and the relation of soil N-access and herbage yield in temporary agricultural grasslands. Therefore, the objective of the present work was to test the hypotheses 1) that a mixture comprising both shallow- and deep-rooting plant species has greater herbage yields than a shallow-rooting binary mixture and pure stands, 2) that deep-rooting plant species (chicory and lucerne) are superior in accessing soil N from 1.2 m soil depth compared with shallow-rooting plant species, 3) that shallow-rooting plant species (perennial ryegrass and white clover) are superior in accessing soil N from 0.4 m soil depth compared with deep-rooting plant species, 4) that a mixture of deep- and shallow-rooting plant species has greater access to soil N from three soil layers compared with a shallow-rooting two-species mixture and that 5) the leguminous grassland plants, lucerne and white clover, have a strong impact on grassland N acquisition, because of their ability to derive N from the soil and the atmosphere

Counter-current chromatography for the separation of terpenoids: A comprehensive review with respect to the solvent systems employed

Copyright @ 2014 The Authors.This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Natural products extracts are commonly highly complex mixtures of active compounds and consequently their purification becomes a particularly challenging task. The development of a purification protocol to extract a single active component from the many hundreds that are often present in the mixture is something that can take months or even years to achieve, thus it is important for the natural product chemist to have, at their disposal, a broad range of diverse purification techniques. Counter-current chromatography (CCC) is one such separation technique utilising two immiscible phases, one as the stationary phase (retained in a spinning coil by centrifugal forces) and the second as the mobile phase. The method benefits from a number of advantages when compared with the more traditional liquid-solid separation methods, such as no irreversible adsorption, total recovery of the injected sample, minimal tailing of peaks, low risk of sample denaturation, the ability to accept particulates, and a low solvent consumption. The selection of an appropriate two-phase solvent system is critical to the running of CCC since this is both the mobile and the stationary phase of the system. However, this is also by far the most time consuming aspect of the technique and the one that most inhibits its general take-up. In recent years, numerous natural product purifications have been published using CCC from almost every country across the globe. Many of these papers are devoted to terpenoids-one of the most diverse groups. Naturally occurring terpenoids provide opportunities to discover new drugs but many of them are available at very low levels in nature and a huge number of them still remain unexplored. The collective knowledge on performing successful CCC separations of terpenoids has been gathered and reviewed by the authors, in order to create a comprehensive document that will be of great assistance in performing future purifications. © 2014 The Author(s)

Plant Species Loss Affects Life-History Traits of Aphids and Their Parasitoids

The consequences of plant species loss are rarely assessed in a multi-trophic context and especially effects on life-history traits of organisms at higher trophic levels have remained largely unstudied. We used a grassland biodiversity experiment and measured the effects of two components of plant diversity, plant species richness and the presence of nitrogen-fixing legumes, on several life-history traits of naturally colonizing aphids and their primary and secondary parasitoids in the field. We found that, irrespective of aphid species identity, the proportion of winged aphid morphs decreased with increasing plant species richness, which was correlated with decreasing host plant biomass. Similarly, emergence proportions of parasitoids decreased with increasing plant species richness. Both, emergence proportions and proportions of female parasitoids were lower in plots with legumes, where host plants had increased nitrogen concentrations. This effect of legume presence could indicate that aphids were better defended against parasitoids in high-nitrogen environments. Body mass of emerged individuals of the two most abundant primary parasitoid species was, however, higher in plots with legumes, suggesting that once parasitoids could overcome aphid defenses, they could profit from larger or more nutritious hosts. Our study demonstrates that cascading effects of plant species loss on higher trophic levels such as aphids, parasitoids and secondary parasitoids begin with changed life-history traits of these insects. Thus, life-history traits of organisms at higher trophic levels may be useful indicators of bottom-up effects of plant diversity on the biodiversity of consumers

Using Plant Functional Traits to Explain Diversity–Productivity Relationships

Background: The different hypotheses proposed to explain positive species richness–productivity relationships, i.e. selection effect and complementarity effect, imply that plant functional characteristics are at the core of a mechanistic understanding of biodiversity effects. Methodology/Principal Findings: We used two community-wide measures of plant functional composition, (1) community- weighted means of trait values (CWM) and (2) functional trait diversity based on Rao’s quadratic diversity (FDQ) to predict biomass production and measures of biodiversity effects in experimental grasslands (Jena Experiment) with different species richness (2, 4, 8, 16 and 60) and different functional group number and composition (1 to 4; legumes, grasses, small herbs, tall herbs) four years after establishment. Functional trait composition had a larger predictive power for community biomass and measures of biodiversitity effects (40–82% of explained variation) than species richness per se (,1–13% of explained variation). CWM explained a larger amount of variation in community biomass (80%) and net biodiversity effects (70%) than FDQ (36 and 38% of explained variation respectively). FDQ explained similar proportions of variation in complementarity effects (24%, positive relationship) and selection effects (28%, negative relationship) as CWM (27% of explained variation for both complementarity and selection effects), but for all response variables the combination of CWM and FDQ led to significant model improvement compared to a separate consideration of different components of functional trait composition. Effects of FDQ were mainly attributable to diversity in nutrient acquisition and life-history strategies. The large spectrum of traits contributing to positive effects of CWM on biomass production and net biodiversity effects indicated that effects of dominant species were associated with different trait combinations. Conclusions/Significance: Our results suggest that the identification of relevant traits and the relative impacts of functional identity of dominant species and functional diversity are essential for a mechanistic understanding of the role of plant diversity for ecosystem processes such as aboveground biomass production

How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required

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