Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves

Individual processes shaping geographical patterns of biodiversity are increasingly understood, but their complex interactions on broad spatial and temporal scales remain beyond the reach of analytical models and traditional experiments. To meet this challenge, we built a spatially explicit, mechanistic simulation model implementing adaptation, range shifts, fragmentation, speciation, dispersal, competition, and extinction, driven by modeled climates of the past 800,000 years in South America. Experimental topographic smoothing confirmed the impact of climate heterogeneity on diversification. The simulations identified regions and episodes of speciation (cradles), persistence (museums), and extinction (graves). Although the simulations had no target pattern and were not parameterized with empirical data, emerging richness maps closely resembled contemporary maps for major taxa, confirming powerful roles for evolution and diversification driven by topography and climate

Climate dynamics and fluid mechanics: Natural variability and related uncertainties

The purpose of this review-and-research paper is twofold: (i) to review the role played in climate dynamics by fluid-dynamical models; and (ii) to contribute to the understanding and reduction of the uncertainties in future climate-change projections. To illustrate the first point, we focus on the large-scale, wind-driven flow of the mid-latitude oceans which contribute in a crucial way to Earth's climate, and to changes therein. We study the low-frequency variability (LFV) of the wind-driven, double-gyre circulation in mid-latitude ocean basins, via the bifurcation sequence that leads from steady states through periodic solutions and on to the chaotic, irregular flows documented in the observations. This sequence involves local, pitchfork and Hopf bifurcations, as well as global, homoclinic ones. The natural climate variability induced by the LFV of the ocean circulation is but one of the causes of uncertainties in climate projections. Another major cause of such uncertainties could reside in the structural instability in the topological sense, of the equations governing climate dynamics, including but not restricted to those of atmospheric and ocean dynamics. We propose a novel approach to understand, and possibly reduce, these uncertainties, based on the concepts and methods of random dynamical systems theory. As a very first step, we study the effect of noise on the topological classes of the Arnol'd family of circle maps, a paradigmatic model of frequency locking as occurring in the nonlinear interactions between the El Nino-Southern Oscillations (ENSO) and the seasonal cycle. It is shown that the maps' fine-grained resonant landscape is smoothed by the noise, thus permitting their coarse-grained classification. This result is consistent with stabilizing effects of stochastic parametrization obtained in modeling of ENSO phenomenon via some general circulation models.Comment: Invited survey paper for Special Issue on The Euler Equations: 250 Years On, in Physica D: Nonlinear phenomen

A Topical Approach to Capturing Customer Insight In Social Media

The age of social media has opened new opportunities for businesses. This flourishing wealth of information is outside traditional channels and frameworks of classical marketing research, including that of Marketing Mix Modeling (MMM). Textual data, in particular, poses many challenges that data analysis practitioners must tackle. Social media constitute massive, heterogeneous, and noisy document sources. Industrial data acquisition processes include some amount of ETL. However, the variability of noise in the data and the heterogeneity induced by different sources create the need for ad-hoc tools. Put otherwise, customer insight extraction in fully unsupervised, noisy contexts is an arduous task. This research addresses the challenge of fully unsupervised topic extraction in noisy, Big Data contexts. We present three approaches we built on the Variational Autoencoder framework: the Embedded Dirichlet Process, the Embedded Hierarchical Dirichlet Process, and the time-aware Dynamic Embedded Dirichlet Process. These nonparametric approaches concerning topics present the particularity of determining word embeddings and topic embeddings. These embeddings do not require transfer learning, but knowledge transfer remains possible. We test these approaches on benchmark and automotive industry-related datasets from a real-world use case. We show that our models achieve equal to better performance than state-of-the-art methods and that the field of topic modeling would benefit from improved evaluation metrics

Mapping the Climate Communication Research Landscape

Climate communication today seems to be at a point of reinvention. The recent rapid growth of the field and its disciplinary diversity have produced a profusion of evidence-based techniques and theories for communicating climate science and climate change, but no definitive answer on how to move the needle on climate action. A core challenge for the field at present is how to make this abundance of research accessible and usable for practitioners, so that opportunities for impact are not missed. Answering calls in the literature for synoptic perspectives on areas of science communication, I use bibliometric network analysis, topic modeling, and knowledge mapping techniques to create and analyze maps of the climate communication research landscape as represented by 2,995 publications about climate communication from Web of Science. Knowledge maps are structural and visual portraits of scholarship which are useful for identifying areas of opportunity and coordinating effort in interdisciplinary and action-oriented knowledge domains. The knowledge maps themselves reveal dense webs of connection among five distinct knowledge communities, indicating an intensely collaborative knowledge domain, and suggest new avenues for application of climate communication knowledge, in particular to support climate services and co-production. After presenting the results of the knowledge mapping study, I discuss ethical and practical challenges encountered in developing these knowledge maps and the strategies I employed to overcome them, adding to the methodological literature on this subject. Taken together, the three chapters of this dissertation represent a conversation about the structure of climate communication research and the tools required for discovering, depicting, and understanding that structure. The contribution of this work overall is to offer a fixed vantage point from which to study past and current state-of-the art climate communication, and the knowledge structure that supports it. This analysis can act as a benchmark for where climate communication is now, and a tool for recognizing when and how the field has grown beyond its current structure

Impacts of the Changing Pacific on North American Drought, Atmospheric Rivers, and Explosive Cyclones

The impacts of specific weather events can vary greatly from year to year. Much of these impacts depend heavily on the frequency of impactful weather which is constrained by the state of the climate system each year. This research focuses largely on the impacts that climate oscillations from year-to-year or even from decade-to-decade have on the frequency of impactful weather. There are numerous examples of impactful weather that impact North America, but this work focuses on drought in the western United States, atmospheric rivers in Northern California and rapidly developing winter storms along the east coast. While seemingly disparate events, there is much overlap in the mechanisms by which variations in the ocean and atmosphere can impact the frequency of these impactful events. Most of these mechanisms involve the tropical Pacific Ocean, which acts as a major driving force for the state of the atmosphere over North America and the resulting frequency of weather extremes

Machine learning to generate soil information

This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large data compilations. This compilation of these global datasets is severely limited by privacy concerns and, currently, no solution has been implemented to facilitate the process. If we consider the performance differences derived from the generality of global models versus the specificity of local models, there is still a debate on which approach is better. Either in global or local DSM, most applications are static. Even with the large soil datasets available to date, there is not enough soil data to perform a fully-empirical, space-time modelling. Considering these knowledge gaps, this thesis aims to introduce advanced ML algorithms and training techniques, specifically deep neural networks, for modelling large datasets at a global scale and provide new soil information. The research presented here has been successful at applying the latest advances in ML to improve upon some of the current approaches for soil modelling with large datasets. It has also created opportunities to utilise information, such as descriptive data, that has been generally disregarded. ML methods have been embraced by the soil community and their adoption is increasing. In the particular case of neural networks, their flexibility in terms of structure and training makes them a good candidate to improve on current soil modelling approaches