Ecological and evolutionary processes at expanding range margins

Many animals are regarded as relatively sedentary and specialized in marginal parts of their geographical distributions. They are expected to be slow at colonizing new habitats. Despite this, the cool margins of many species' distributions have expanded rapidly in association with recent climate warming. We examined four insect species that have expanded their geographical ranges in Britain over the past 20 years. Here we report that two butterfly species have increased the variety of habitat types that they can colonize, and that two bush cricket species show increased fractions of longer-winged (dispersive) individuals in recently founded populations. Both ecological and evolutionary processes are probably responsible for these changes. Increased habitat breadth and dispersal tendencies have resulted in about 3- to 15-fold increases in expansion rates, allowing these insects to cross habitat disjunctions that would have represented major or complete barriers to dispersal before the expansions started. The emergence of dispersive phenotypes will increase the speed at which species invade new environments, and probably underlies the responses of many species to both past and future climate change

Non-random dispersal in the butterfly Maniola jurtina: implications for metapopulation models

The dispersal patterns of animals are important in metapopulation ecology because they affect the dynamics and survival of populations. Theoretical models assume random dispersal but little is known in practice about the dispersal behaviour of individual animals or the strategy by which dispersers locate distant habitat patches. In the present study, we released individual meadow brown butterflies (Maniola jurtina) in a non-habitat and investigated their ability to return to a suitable habitat. The results provided three reasons for supposing that meadow brown butterflies do not seek habitat by means of random flight. First, when released within the range of their normal dispersal distances, the butterflies orientated towards suitable habitat at a higher rate than expected at random. Second, when released at larger distances from their habitat, they used a non-random, systematic, search strategy in which they flew in loops around the release point and returned periodically to it. Third, butterflies returned to a familiar habitat patch rather than a non-familiar one when given a choice. If dispersers actively orientate towards or search systematically for distant habitat, this may be problematic for existing metapopulation models, including models of the evolution of dispersal rates in metapopulations

Impacts of global change on water-related sectors and society in a trans-boundary central European river basin – Part 2: From eco-hydrology to water demand management

This second part of the paper presents the details of the eco-hydrological model SWIM simulating the natural water supply and its coupling to WBalMo, a water management model. Based on the climate scenarios of the STAR model, SWIM simulates the natural water and matter fluxes for the entire Elbe River area. All relevant processes are modelled for hydrotopes and the resulting discharges are accumulated in subbasins. The output data are input for the water management model WBalMo and the quality models Moneris and QSim. WBalMo takes storage management, inputs and withdrawals into account and analyses how demands by industry, power plants and households will be met at changing natural supply conditions. Some of the first results shall be presented here

Eukaryotic Translation Initiation Factor 5B Activity Regulates Larval Growth Rate and Germline Development in Caenorhabditis elegans

In C. elegans, a population of proliferating germ cells is maintained via GLP-1/Notch signaling; in the absence of GLP-1 signaling, germ cells prematurely enter meiosis and differentiate. We previously identified ego (enhancer of glp-1) genes that promote germline proliferation and interact genetically with the GLP-1 signaling pathway. Here, we report that iffb-1 (initiation factor five B) is an ego gene. iffb-1 encodes the sole C. elegans isoform of eukaryotic translation initiation factor 5B, a protein essential for translation. We have used RNA interference and a deletion mutation to determine the developmental consequences of reduced iffb-1 activity. Our data indicate that maternal iffb-1 gene expression is sufficient for embryogenesis, and zygotic iffb-1 expression is required for development beyond late L1/ early L2 stage. Partial reduction in iffb-1 expression delays larval development and can severely disrupt proliferation and differentiation of germ cells. We hypothesize that germline development is particularly sensitive to iffb-1 expression level

Impacts of global change on water-related sectors and society in a trans-boundary central European river basin – Part 1: Project framework and impacts on agriculture

Central Europe, the focus region of this study, is a region in transition, climatically from maritime to continental and politically from formerly more planning-oriented to more market-oriented management regimes, and in terms of climate change from regions of increasing precipitation in the west and north of Europe to regions of decreasing precipitation in central and southern Europe. The Elbe basin, a trans-boundary catchment flowing from the Czech Republic through Germany into the North Sea, was selected to investigate the possible impacts of global change on crop yields and water resources in this region. For technical reasons, the paper has been split into two parts, the first showing the overall model concept, the model set-up for the agricultural sector, and first results linking eco-hydrological and agro-economic tools for the German part of the basin. The second part describes the model set-up for simulating water supply and demand linking eco-hydrological and water management tools for the entire basin including the Czech part

Automated Plankton Classification With a Dynamic Optimization and Adaptation Cycle

With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis

Sialoglycoproteins and N-Glycans from Secreted Exosomes of Ovarian Carcinoma Cells

Exosomes consist of vesicles that are secreted by several human cells, including tumor cells and neurons, and they are found in several biological fluids. Exosomes have characteristic protein and lipid composition, however, the results concerning glycoprotein composition and glycosylation are scarce. Here, protein glycosylation of exosomes from ovarian carcinoma SKOV3 cells has been studied by lectin blotting, NP-HPLC analysis of 2-aminobenzamide labeled glycans and mass spectrometry. An abundant sialoglycoprotein was found enriched in exosomes and it was identified by peptide mass fingerprinting and immunoblot as the galectin-3-binding protein (LGALS3BP). Exosomes were found to contain predominantly complex glycans of the di-, tri-, and tetraantennary type with or without proximal fucose and also high mannose glycans. Diantennary glycans containing bisecting N-acetylglucosamine were also detected. This work provides detailed information about glycoprotein and N-glycan composition of exosomes from ovarian cancer cells, furthermore it opens novel perspectives to further explore the functional role of glycans in the biology of exosomes.EU Joint Programme JPND/0003/2011, FCT grant: Pest-OE/EQB/LA0004/2011, FCT PhD fellowship

A Mathematical Model for the Dynamics and Synchronization of Cows

We formulate a mathematical model for daily activities of a cow (eating, lying down, and standing) in terms of a piecewise affine dynamical system. We analyze the properties of this bovine dynamical system representing the single animal and develop an exact integrative form as a discrete-time mapping. We then couple multiple cow "oscillators" together to study synchrony and cooperation in cattle herds. We comment on the relevant biology and discuss extensions of our model. With this abstract approach, we not only investigate equations with interesting dynamics but also develop interesting biological predictions. In particular, our model illustrates that it is possible for cows to synchronize \emph{less} when the coupling is increased.Comment: to appear in Physica

MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology

While the analysis of mitochondrial morphology has emerged as a key tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a challenging task and bottleneck for statistically robust conclusions. Here, we present Mitochondrial Segmentation Network (MitoSegNet), a pretrained deep learning segmentation model that enables researchers to easily exploit the power of deep learning for the quantification of mitochondrial morphology. We tested the performance of MitoSegNet against three feature-based segmentation algorithms and the machine-learning segmentation tool Ilastik. MitoSegNet outperformed all other methods in both pixelwise and morphological segmentation accuracy. We successfully applied MitoSegNet to unseen fluorescence microscopy images of mitoGFP expressing mitochondria in wild-type and catp-6ATP13A2 mutant C. elegans adults. Additionally, MitoSegNet was capable of accurately segmenting mitochondria in HeLa cells treated with fragmentation inducing reagents. We provide MitoSegNet in a toolbox for Windows and Linux operating systems that combines segmentation with morphological analysis

Prenatal Predictors of Infant Self-Regulation: The Contributions of Placental DNA Methylation of NR3C1 and Neuroendocrine Activity

We examined whether placental DNA methylation of the glucocorticoid receptor gene, NR3C1 was associated with self-regulation and neuroendocrine responses to a social stressor in infancy. Placenta samples were obtained at birth and mothers and their infants (n = 128) participated in the still-face paradigm when infants were 5 months old. Infant self-regulation following the still-face episode was coded and pre-stress cortisol and cortisol reactivity was assessed in response to the still-face paradigm. A factor analysis of NR3C1 CpG sites revealed two factors: one for CpG sites 1-4 and the other for sites 5-13. DNA methylation of the factor comprising NR3C1 CpG sites 5-13 was related to greater cortisol reactivity and infant self-regulation, but cortisol reactivity was not associated with infant self-regulation. The results reveal that prenatal epigenetic processes may explain part of the development of infant self-regulation