Mechanisms of plant diversity-productivity relationships in complex ecological networks

The importance of biodiversity for providing ecosystem functions and services crucial to human well-being is well documented. However, the mechanisms underlying biodiversity-ecosystem functioning (BEF) relationships are insufficiently understood. I address this issue by zooming in on plant diversity-productivity relationships and their potential drivers. Specifically, I combine theoretical and empirical approaches focusing on resource- and animal-based processes, as well as their joint effect on plant community composition and productivity. Additionally, I consider spatial aspects of resource-based, animal-based, and generalized empirical interactions. My findings show that plant diversity only affects productivity positively when plants have complementarity resource requirements and spatially overlap in their resource access, thereby risking negative effects from competition. This includes the competitive exclusion of inferior competitors. However, in complex food webs, competitive exclusion of plant species is largely mitigated. This aligns with my empirical findings that suggest coexistence mechanisms playing a central role in driving plant diversity-productivity relationships. Despite positive effects on plant coexistence, animal-based processes induce strong variation on plant diversity-productivity relationships. Specifically, unconstrained animal movement can lead to negative relationships, whereas movement scaled with animal body masses turns them positive. My findings further suggest considerable interactive effects between resource- and animal-based mechanisms. However, clear differences in how either mechanism assembles plant communities could serve as a tool to disentangle them. This opens a way to focus conservation and restoration efforts that counteract the global biodiversity crisis, ensuring the provisioning of ecosystem service that are crucial to human society

ART: A Data Aggregation Program for the Behavioral Sciences

Today, many experiments in the field of behavioral sciences are conducted using a computer. While there is a broad choice of computer programs facilitating the process of conducting experiments as well as programs for statistical analysis there are relatively few programs facilitating the intermediate step of data aggregation. ART has been developed in order to fill this gap and to provide a computer program for data aggregation that has a graphical user interface such that aggregation can be done more easily and without any programming. All “rules” that are necessary to extract variables can be seen “at a glance” which helps the user to conduct even complex aggregations with several hundreds of variables and makes aggregation more resistant against errors. ART runs with Windows XP, Vista, 7, and 8 and it is free. Copies (executable and source code) are available at http://www.psychologie.hhu.de/arbeitsgruppen/allgemeinepsychologie-und-arbeitspsychologie/art.html

Kant's treatment of the arguments of God

Thesis (M.A.)--Boston University, 1936. This item was digitized by the Internet Archive

Resolved-sideband cooling and measurement of a micromechanical oscillator close to the quantum limit

The observation of quantum phenomena in macroscopic mechanical oscillators has been a subject of interest since the inception of quantum mechanics. Prerequisite to this regime are both preparation of the mechanical oscillator at low phonon occupancy and a measurement sensitivity at the scale of the spread of the oscillator's ground state wavefunction. It has been widely perceived that the most promising approach to address these two challenges are electro nanomechanical systems. Here we approach for the first time the quantum regime with a mechanical oscillator of mesoscopic dimensions--discernible to the bare eye--and 1000-times more massive than the heaviest nano-mechanical oscillators used to date. Imperative to these advances are two key principles of cavity optomechanics: Optical interferometric measurement of mechanical displacement at the attometer level, and the ability to use measurement induced dynamic back-action to achieve resolved sideband laser cooling of the mechanical degree of freedom. Using only modest cryogenic pre-cooling to 1.65 K, preparation of a mechanical oscillator close to its quantum ground state (63+-20 phonons) is demonstrated. Simultaneously, a readout sensitivity that is within a factor of 5.5+-1.5 of the standard quantum limit is achieved. The reported experiments mark a paradigm shift in the approach to the quantum limit of mechanical oscillators using optical techniques and represent a first step into a new era of experimental investigation which probes the quantum nature of the most tangible harmonic oscillator: a mechanical vibration.Comment: 14 pages, 4 figure

A Voting Approach for Explainable Classification with Rule Learning

State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance. Contrarily, in this paper, we investigate the application of rule learning methods in such a context. Thus, classifications become based on comprehensible (first-order) rules, explaining the predictions made. In general, however, rule-based classifications are less accurate than state-of-the-art results (often significantly). As main contribution, we introduce a voting approach combining both worlds, aiming to achieve comparable results as (unexplainable) state-of-the-art methods, while still providing explanations in the form of deterministic rules. Considering a variety of benchmark data sets including a use case of significant interest to insurance industries, we prove that our approach not only clearly outperforms ordinary rule learning methods, but also yields results on a par with state-of-the-art outcomes.Comment: 34 pages, 10 figure