Inventing Organizations of the 21st Century: Producing Knowledge Through Collaboration

This manuscript examines a Process Handbook (PH) special project using a learning history form. A learning history is an assessment-for-learning, designed such that its value is derived when read and discussed by teams interested in similar issues. Its contents come from the people who initiated, implemented, and participated in the documented efforts as well as non-participants who were affected by it. A learning history presents the experiences and understandings of people who have gone through a learning effort in their own words, in a way that helps others move forward without having to "re-invent" what the original group of learners discovered. The content of the learning history creates a context for conversation that teams within organizations wouldn't be able to have otherwise. This learning history, and the PH project it describes, raises issues around knowledge creation and team structures by looking at how a project team of individuals from university, business, and consulting organizations was effective in creating new knowledge. The team members held different predispositions toward theory development, producing business outcomes, and developing capacity for action. Their complementary, and at times conflicting, interests provided a robust structure for knowledge creation. Knowledge created through this team structure is also multidimensional, having theoretical, methodological, and practical components.

Inverted initial conditions: exploring the growth of cosmic structure and voids

We introduce and explore "paired" cosmological simulations. A pair consists of an A and B simulation with initial conditions related by the inversion $\delta_A(x, t_{initial})=-\delta_B(x,t_{initial})$ (underdensities substituted for overdensities and vice versa). We argue that the technique is valuable for improving our understanding of cosmic structure formation. The A and B fields are by definition equally likely draws from {\Lambda}CDM initial conditions, and in the linear regime evolve identically up to the overall sign. As non-linear evolution takes hold, a region that collapses to form a halo in simulation A will tend to expand to create a void in simulation B. Applications include (i) contrasting the growth of A-halos and B-voids to test excursion-set theories of structure formation; (ii) cross-correlating the density field of the A and B universes as a novel test for perturbation theory; and (iii) canceling error terms by averaging power spectra between the two boxes. Generalizations of the method to more elaborate field transformations are suggested.Comment: 10 pages (including appendix), 6 figures. To be submitted to PR

Challenges of theoretical and numerical structure formation in a ΛCDM universe

Systematic surveys of the extra-galactic sky have revealed the existence of large-scale structures in the Universe: the galaxy distribution is organized in a complex network of filaments surrounding underdense regions and crossing at density peaks which host galaxy clusters. These structures are believed to form through gravitational instability starting from quantum density fluctuations in the primordial Universe.Perhaps surprisingly -- considering the huge dynamical range and complexity of the Universe -- recent years have witnessed the emergence of a cosmological concordance model, dubbed ΛCDM. The first two chapters provide a summary of the theoretical background of the components of this model. During the last two decades, numerical simulations have become a powerful tool to study astronomical objects on a large range of scales. They are used extensively in all areas of astronomy and astrophysics: e.g. the formation of single stars and planets, analyzing the dynamics of star clusters, modeling hydrodynamical processes in galaxies like our Milky Way, and last but not least studying the large-scale structure of cosmological volumes. We describe the general methodology of cosmological N-body simulations, its strengths and limitations, and the specific implementation used in this work, the Gadget-2 code. The following chapters rely heavily on results of such simulations, as they can be used to test theoretical predictions or compare the ΛCDM model with observational data. After this technical introduction, we describe a novel numerical method to test perturbative methods for computing the density contrast of dark matter, and compare the result against full N-body simulations. In addition, we test the validity of a popular bias model and find that it lacks the accuracy required to fully exploit the statistical power of upcoming galaxy surveys. The next chapter deals with local primordial non-Gaussianity (PNG), a specific type of initial conditions for structure formation. While the most stringent constraints on primordial non-Gaussianity currently come from the cosmic microvwave background (CMB), galaxy clustering provides an independent validation of these results, and future large-scale structure surveys are even predicted to surpass the CMB constraints. However, we show that galaxy clustering suffers from specific parameter degeneracies which are not present in the CMB. We caution against the commonly used simple model to measure PNG parameters, and instead promote Bayesian model selection to asses the influence of these degeneracies in the data. The final chapter addresses the subject of near-field cosmology, i.e. the concept of supplementing the constraints on the overall cosmological model with small-scale information from the local Universe. In particular, this chapter deals with the dynamical properties of simulated subhalos around high-resolution Milky-Way sized dark matter halos, representative of the population of dwarf galaxies around our own Galaxy. We investigate the physical conditions needed to rapidly circularize the orbits of infalling systems, possibly giving rise to peculiar stellar streams such as the recently observed Monoceros overdensity

Milking the spherical cow: on aspherical dynamics in spherical coordinates

Galaxies and the dark matter halos that host them are not spherically symmetric, yet spherical symmetry is a helpful simplifying approximation for idealised calculations and analysis of observational data. The assumption leads to an exact conservation of angular momentum for every particle, making the dynamics unrealistic. But how much does that inaccuracy matter in practice for analyses of stellar distribution functions, collisionless relaxation, or dark matter core-creation? We provide a general answer to this question for a wide class of aspherical systems; specifically, we consider distribution functions that are "maximally stable", i.e. that do not evolve at first order when external potentials (which arise from baryons, large scale tidal fields or infalling substructure) are applied. We show that a spherically-symmetric analysis of such systems gives rise to the false conclusion that the density of particles in phase space is ergodic (a function of energy alone). Using this idea we are able to demonstrate that: (a) observational analyses that falsely assume spherical symmetry are made more accurate by imposing a strong prior preference for near-isotropic velocity dispersions in the centre of spheroids; (b) numerical simulations that use an idealised spherically-symmetric setup can yield misleading results and should be avoided where possible; and (c) triaxial dark matter halos (formed in collisionless cosmological simulations) nearly attain our maximally-stable limit, but their evolution freezes out before reaching it.Comment: Submitted to MNRAS. Comments welcom

Modeling the effect of roads and topoclimate on plant invasions in mountains : the case of Trifolium repens

Non-native species are a main cause for the global loss of biodiversity and ecosystem services. Mountain regions have been relatively spared from plant invasions up to now, mostly due to climatic restrictions and low human influence, with roads being the main pathways. But the invasion risk is increasing due to climate change and intensified land use. This is problematic, because mountain regions generally have high conservation value and are hard to manage. Therefore, prevention is crucial. The aim of my study is to improve predictions of plant invasions by including roads and topoclimate in the species distribution models and to provide suggestions for adequate roadside management. First, I downscaled bioclimatic variables according to the topography to a resolution of 50 x 50 m, applying a geographically weighted regression. Then, I fitted a species distribution model on both the original and downscaled bioclimate (‘topoclimate’), with a generalized linear mixed model. As response variable, I used presence-absence data for Trifolium repens (n=7683), which had been collected by the Mountain Invasion Research Network in 11 mountain regions worldwide. Furthermore, I fitted three species distribution models, based on ‘topoclimate’, ‘topoclimate and roads’ and ‘roads’. I then evaluated all models with the area under the receiver operating characteristic curve, focusing especially on sensitivity values. For validation, I used an independent dataset from Victoria, Australia. Both downscaling the bioclimate and including roads improved the species distribution models, with roads being an even more robust predictor than bioclimate. However, the overall predictive power of all models was very low, with moderate sensitivity values. This limited predictive power on a regional level (in Victoria, Australia) can be partly explained by general issues regarding invasive species but also by local peculiarities of the validation area. More local information would be needed in order to make accurate predictions for regional management. However, the global importance of mountain roads as pathways for plant invasions was confirmed by my study, which emphasizes the need for adequate roadside management. Generally, management should focus on both preventive measures as well as controlling further spread, especially in high conservation value areas