Density-dependent, central-place foraging in a grazing herbivore: competition and tradeoffs in time allocation near water

Optimal foraging theory addresses one of the core challenges of ecology: predicting the distribution and abundance of species. Tests of hypotheses of optimal foraging, however, often focus on a single conceptual model rather than drawing upon the collective body of theory, precluding generalization. Here we demonstrate links between two established theoretical frameworks predicting animal movements and resource use: central-place foraging and density-dependent habitat selection. Our goal is to better understand how the nature of critical, centrally placed resources like water (or minerals, breathing holes, breeding sites, etc.) might govern selection for food (energy) resources obtained elsewhere - a common situation for animals living in natural conditions. We empirically test our predictions using movement data from a large herbivore distributed along a gradient of water availability (feral horses, Sable Island, Canada, 2008–2013). Horses occupying western Sable Island obtain freshwater at ponds while in the east horses must drink at self-excavated wells (holes). We studied the implications of differential access to water (time needed for a horse to obtain water) on selection for vegetation associations. Consistent with predictions of density-dependent habitat selection, horses were reduced to using poorer-quality habitat (heathland) more than expected close to water (where densities were relatively high), but were free to select for higher-quality grasslands farther from water. Importantly, central-place foraging was clearly influenced by the type of water-source used (ponds vs. holes, the latter with greater time constraints on access). Horses with more freedom to travel (those using ponds) selected for grasslands at greater distances and continued to select grasslands at higher densities, whereas horses using water holes showed very strong density-dependence in how habitat could be selected. Knowledge of more than one theoretical framework may be required to explain observed variation in foraging behavior of animals where multiple constraints simultaneously influence resource selection

Evaluation of experimental design and computational parameter choices affecting analyses of ChIP-seq and RNA-seq data in undomesticated poplar trees.

BackgroundOne of the great advantages of next generation sequencing is the ability to generate large genomic datasets for virtually all species, including non-model organisms. It should be possible, in turn, to apply advanced computational approaches to these datasets to develop models of biological processes. In a practical sense, working with non-model organisms presents unique challenges. In this paper we discuss some of these challenges for ChIP-seq and RNA-seq experiments using the undomesticated tree species of the genus Populus.ResultsWe describe specific challenges associated with experimental design in Populus, including selection of optimal genotypes for different technical approaches and development of antibodies against Populus transcription factors. Execution of the experimental design included the generation and analysis of Chromatin immunoprecipitation-sequencing (ChIP-seq) data for RNA polymerase II and transcription factors involved in wood formation. We discuss criteria for analyzing the resulting datasets, determination of appropriate control sequencing libraries, evaluation of sequencing coverage needs, and optimization of parameters. We also describe the evaluation of ChIP-seq data from Populus, and discuss the comparison between ChIP-seq and RNA-seq data and biological interpretations of these comparisons.ConclusionsThese and other "lessons learned" highlight the challenges but also the potential insights to be gained from extending next generation sequencing-supported network analyses to undomesticated non-model species

Adaptive optimal operation of a parallel robotic liquid handling station

Results are presented from the optimal operation of a fully automated robotic liquid handling station where parallel experiments are performed for calibrating a kinetic fermentation model. To increase the robustness against uncertainties and/or wrong assumptions about the parameter values, an iterative calibration and experiment design approach is adopted. Its implementation yields a stepwise reduction of parameter uncertainties together with an adaptive redesign of reactor feeding strategies whenever new measurement information is available. The case study considers the adaptive optimal design of 4 parallel fed-batch strategies implemented in 8 mini-bioreactors. Details are given on the size and complexity of the problem and the challenges related to calibration of over-parameterized models and scarce and non-informative measurement data. It is shown how methods for parameter identifiability analysis and numerical regularization can be used for monitoring the progress of the experimental campaigns in terms of generated information regarding parameters and selection of the best fitting parameter subset.BMBF, 02PJ1150, Verbundprojekt: Plattformtechnologien für automatisierte Bioprozessentwicklung (AutoBio); Teilprojekt: Automatisierte Bioprozessentwicklung am Beispiel von neuen Nukleosidphosphorylase

Submodular Load Clustering with Robust Principal Component Analysis

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.Comment: Accepted by 2019 IEEE PES General Meeting, Atlanta, G