Adaptive processes drive ecomorphological convergent evolution in antwrens (Thamnophilidae)

© 2014 The Author(s). Phylogenetic niche conservatism (PNC) and convergence are contrasting evolutionary patterns that describe phenotypic similarity across independent lineages. Assessing whether and how adaptive processes give origin to these patterns represent a fundamental step toward understanding phenotypic evolution. Phylogenetic model-based approaches offer the opportunity not only to distinguish between PNC and convergence, but also to determine the extent that adaptive processes explain phenotypic similarity. The Myrmotherula complex in the Neotropical family Thamnophilidae is a polyphyletic group of sexually dimorphic small insectivorous forest birds that are relatively homogeneous in size and shape. Here, we integrate a comprehensive species-level molecular phylogeny of the Myrmotherula complex with morphometric and ecological data within a comparative framework to test whether phenotypic similarity is described by a pattern of PNC or convergence, and to identify evolutionary mechanisms underlying body size and shape evolution. We show that antwrens in the Myrmotherula complex represent distantly related clades that exhibit adaptive convergent evolution in body size and divergent evolution in body shape. Phenotypic similarity in the group is primarily driven by their tendency to converge toward smaller body sizes. Differences in body size and shape across lineages are associated to ecological and behavioral factors

Multi-objective and multi-variate global sensitivity analysis of the soil-crop model XN-CERES in Southwest Germany

Soil-crop models enjoy ever-greater popularity as tools to assess the im- pact of environmental changes or management strategies on agricultural production. Soil-crop models are designed to coherently simulate the crop, nitrogen (N) and water dynamics of agricultural fields. However, soil-crop models depend on a vast number of uncertain model inputs, i.e., initial conditions and parameters. To assess the uncertainty in the simulation results (UCSR) and how they can be apportioned among the model inputs of the XN-CERES soil-crop model, an uncertainty and global sensitivity analysis (GSA) was conducted. We applied two different GSA methods, moment-independent and variance-based methods in the sense of the Factor Prioritization and the Factor Fixing setting. The former identifies the key drivers of uncertainty, i.e., which model input, if fixed to its true value, would lead to the greatest reduction of the UCSR. The latter identifies the model inputs that cannot be fixed at any value within their value range without affecting the UCSR. In total we calculated six sensitivity indices (SIs). The overall objective was to assess the cross-sub-model impact of parameters and the overall determinability of the XN-CERES applied on a deep loess soil profile in Southwest Germany. Therefore, we selected 39 parameters and 16 target variables (TGVs) to be included in the GSA. Furthermore, we assessed a weekly time series of the parameter sensitivities. The sub-models were crop, water, nitrogen and flux. In addition, we also compared moment-independent (MI) and variance-based (VB) GSA methods for their suitability for the two settings. The results show that the parameters of the TGVs of the four groups cannot be considered independently. Each group is impacted by the parameters of the other groups. Crop parameters are most important, followed by the Mualem van Genuchten (MvG) parameters. The nitrate (NO3-) content and the matric potential are the two TGVs that are most affected by the inter- action of parameters, especially crop and MvG parameters. However, the model output of these two TGVs is highly skewed and leptokrutic. Therefore, the variance is an unsuitable representation of the UCSR, and the reliability of the variance-based sensitivity indices SIVB is curtailed. Nitrogen group parameters play an overall minor role for the uncertainty of the whole XN-CERES, but nitrification rates can be calibrated on ammonium (NH4+) measurements. Considering the initial conditions shows the high importance of the initial NO3-; content. If it could be fixed, the uncertainty of crop groups TGVs, the matric potential and the N content in the soil could be reduced. Hence, multi-year predictions of yield suffer from uncertainty due to the simulated NO3-; content. Temporally resolved parameter show the big dependence between the crops development stage and the other 15 TGVs becomes visible. High temporally resolved measurements of the development stage are important to univocally estimate the crop parameters and reduce the uncertainty in the vegetative and generative biomass. Furthermore, potential periods of water and N-limiting situations are assessed, which is helpful for deriving management strategies. In addition, it become clear that measurement campaigns should be conducted at the simulation start and during the vegetation period to have enough information to calibrate the XN-CERES. Regarding the performance of the different GSA methods and the different SIs, we conclude that the sensitivity measure relying on the Kolmogorov-Smirnov metric (betaks) is most stable. It converges quickly and has no issues with highly skewed and leptokrutic model output distributions. The assessments of the first-effect index and the betaks provide information on the additivity of the model and parameters that cannot be fixed without impacting the simulation results. In summary, we could only identify three parameters that have no direct impact on any TGV at any time and are hence not determinable from any measurements of the TGVs considered. Furthermore, we can conclude that the groups parameters should not be calibrated independently because they always affect the uncertainty of the selected TGV directly or via interacting. However, no TGV is suitable to calibrate all parameters. Hence, the calibration of the XN-CERES requires measurements of TGVs from each group, even if the modeler is only interested in one specific TGV, e.g., yield. The GSA should be repeated in a drier climate or with restricted rooting depth. The convergence of the values for the Sobol indices remains an issue. Even larger sample sizes, another convergence criteria or graphical inspection cannot alleviate the issue. However, we can conclude that the sub-models of the XN-CERES cannot be considered in- dependently and that the model does what it is designed for: coherently simulating the crop, N and water dynamics with their interactions.Boden-Pflanze Modelle erfreuen sich immer größerer Beliebtheit, um die Auswirkungen von Umweltveränderungen und Managementstrategien auf die landwirtschaftliche Produktion zu bestimmen. Boden-Pflanzen Modelle sind so konzipiert, dass sie kohärent die Pflanzen-, Stickstoff- (N) und Wasserdynamik in landwirtschaftlichen Feldern simulieren. Leider hängen Boden-Pflanze Modelle von einer Vielzahl unsicherer Modellinputs wie Anfangs- und Randbedingungen sowie Parametern ab. Zur Bestimmung der Unsicherheit in den Simulationsergebnissen (UCSR) und in welchem Ausmaß diese von den Modellinputs des Boden-Pflanze Modells XN-CERES abhängt, wird in dieser Arbeit eine Unsicherheits- und Global Sensitivitäts Analyse (GSA) durchgeführt. Wir verwendeten zwei verschiedene GSA-Methoden, momentunabhängige und varianzbasierte Methoden, im Sinne der Settings: Faktor Priorisierung und Faktor Fixing. Ersteres identifiziert die Parameter, die durch Fixierung zur größten Reduktion der UCSR führen. Letzteres identifiziert die Parameter, die nicht fixiert werden können, ohne die UCSR zu beeinflussen. Insgesamt haben wir sechs verschiedene Sensitivitäts Indices (SIs) berechnet. Das übergeordnete Ziel der Arbeit war es die Teilmodell-übergreifende Wirkung der Parameter und die allgemeinen Bestimmbarkeit des Boden-Pflanzen Modells XN-CERES auf einem Lössstandort in Südwest Deutschland zu quantifizieren. Wir haben insgesamt 39 Parameter und 16 Zielvariablen (TGV) für die GSA ausgewählt. Darüber hinaus lösen wir die Parametersensitivitäten für die vier Teilmodelle Pflanze, Wasser, Stickstoff und Flüsse wöchentlich auf. Darüber hinaus vergleichen wir Moment unabhängige (MI) und Varianz basierte (VB) GSA Methoden und ihre Eignung für die beiden Settings für ein Boden-Pflanze Model. Die Ergebnisse zeigen, dass die Parameter der vier Gruppen im hohen Maße voneinander abhängen. Die Pflanzenparameter haben einen Einfluss auf jede der 16 TGVs. Es folgen die Mualem van Genuchten (MvG) Parameter. Der Nitrat (NO3-) Gehalt und das Matrixpotential am stärksten von Parameterinteraktionen betroffen sind. Allerdings sind die Verteilungen dieser beiden TGVs schief und leptokurtisch. Daher ist die Varianz eine schlechte Repräsentation für die UCSR und die Zuverlässigkeit der Varianz basierten Sensitivitätsindices (SIVB) entsprechend eingeschränkt. Die Parameter der Stickstoffgruppe spielen insgesamt eine untergeordnete Rolle. Die Betrachtung der Anfangsbedingungen zeigt, dass die Unsicherheit in der Simulation der TGVs der Pflanzengruppen, des Matrixpotentials und des N-Gehalts im Boden durch deren akurate Messung stark reduziert werden kann. Vorhersagen für Fruchtfolgen sind folglich unsicher, da der simulierte Ertrag der Hauptfrucht vom Zustand des Bodens nach der Vorfrucht abhängt. Zeitaufgelöste Parametersensitivitäten zeigen die große Abhängigkeit zwischen dem Entwicklungsstadium der Pflanze und den andern 15 TGVs wird sichtbar. Hochauflösende Messungen des Entwicklungsstadiums der Pflanze sind wichtig, um die Pflanzenparameter eindeutig kalibrieren zu können. Ebenfalls können durch zeitaufgelöste Parametersensitivitäten, Zeiträume von möglicher Wasser- und N-Knappheit identifiziert werden. Dies ist besonders wichtig für die Erstellung von Managementstrategien. Messungen sollten vorrangig zu Simulationsbeginn und während der Vegetationsperiode durchgeführt werden, um genügend Informationen für die Kalibrierung des Modells zu erhalten. Bezüglich der Leistung der verschiedenen GSA Methoden und der unterschiedlichen SIs, kommen wir zu dem Ergebnis, dass das auf der Kolmogorov-Smirnov Metrik basierte Sensitivitätsmaß (betaks) am stabilsten ist. Es konvergiert schnell und hat keine Probleme mit stark schiefen und leptokurtischen Verteilungen. Die Kombination aus First-Effect Index und betaks gibt Aufschluss über die Additivität des Modells und identifiziert Parameter, die nicht fixiert werden können. Zusammenfassend lässt sich sagen, dass wir nur drei Parameter identifizieren konnten, die keinen direkten Einfluss auf eine der untersuchten TGV haben. Der direkte Einfluss weiterer acht Parameter ist so gering, dass deren Kalibrierung schwierig ist. Darüber hinaus kommen wir zu dem Schluss, dass die Parameter der verschiedenen Gruppen nicht unabhängig voneinander kalibriert werden können. Weiter ist nicht jede TGV zur Kalibrierung aller Parameter geeignet. Für die Kalibrierung der gewählten Modellkombination sind daher Messungen von TGVs jeder Gruppe erforderlich, auch wenn nur Interesse an einer bestimmten TGV wie zum Beispiel dem Ertrag besteht. Aus der Arbeit ergeben sich einige generelle Empfehlungen. So sollte die GSA in einem trockeneren Klima oder mit eingeschränkter Durchwurzelungstiefe durchgeführt werden. Die Konvergenz der Werte für die Sobol-Indizes ist problematisch. Noch größere Stichprobengrößen, weitere Konvergenzkriterien oder grafische Prüfungen könnten hier Abhilfe schaffen

Automated extraction of mutual independence patterns using Bayesian comparison of partition models

Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models, one being nested into the other and used to generate a null distribution for a statistic of interest, usually under the asymptotic assumption of large sample size. As such, these methods have a very restricted scope of application. In the present manuscript, we propose to change the investigation of mutual independence from a hypothesis-driven task that can only be applied in very specific cases to a blind and automated search within patterns of mutual independence. To this end, we treat the issue as one of model comparison that we solve in a Bayesian framework. We show the relationship between such an approach and existing methods in the case of multivariate normal distributions as well as cross-classified multinomial distributions. We propose a general Markov chain Monte Carlo (MCMC) algorithm to numerically approximate the posterior distribution on the space of all patterns of mutual independence. The relevance of the method is demonstrated on synthetic data as well as two real datasets, showing the unique insight provided by this approach.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (in press

O(N) methods in electronic structure calculations

Linear scaling methods, or O(N) methods, have computational and memory requirements which scale linearly with the number of atoms in the system, N, in contrast to standard approaches which scale with the cube of the number of atoms. These methods, which rely on the short-ranged nature of electronic structure, will allow accurate, ab initio simulations of systems of unprecedented size. The theory behind the locality of electronic structure is described and related to physical properties of systems to be modelled, along with a survey of recent developments in real-space methods which are important for efficient use of high performance computers. The linear scaling methods proposed to date can be divided into seven different areas, and the applicability, efficiency and advantages of the methods proposed in these areas is then discussed. The applications of linear scaling methods, as well as the implementations available as computer programs, are considered. Finally, the prospects for and the challenges facing linear scaling methods are discussed.Comment: 85 pages, 15 figures, 488 references. Resubmitted to Rep. Prog. Phys (small changes

Searching for collective behavior in a network of real neurons

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.Comment: 24 pages, 19 figure

An Architecture Of Connection

This mixed use project explores the senses in order to make nature connections and social connections to truly link people to habitat and place. This is achieved by responding to the contextual input as well as creating new places with consider­ations to intensity, direction, enhancement, blocking, and more

Multiple-Product Firms and Product Switching

This paper examines the frequency, pervasiveness, and determinants of product switching by US manufacturing firms. We find that one-half of firms alter their mix of five-digit SIC products every five years, that product switching is correlated with both firm- and firm-product attributes, and that product adding and dropping induce large changes in firm scope. The behavior we observe is consistent with a natural generalization of existing theories of industry dynamics that incorporates endogenous product selection within firms. Our findings suggest that product switching contributes to a reallocation of resources within firms toward their most efficient use. (JEL L11, L21, L25, L60

Celebration Schedule 2015 (Friday)

Full presentation schedule for Celebration, Friday, May 1, 201

The evolutionary history of Madagascar's tenrecs (mammalia: Tenrecidae): systematics, phylogeography, and species delimitation

Thesis (Ph.D.) University of Alaska Fairbanks, 2018Madagascar is renowned for its exceptionally diverse, unique, and threatened biota, yet much of the island's flora and fauna remains undescribed, and the underlying drivers of diversification and endemism are poorly understood. The family Tenrecidae is one of four extant terrestrial mammal lineages to have colonized and diversified on Madagascar from continental Africa. The goal of this dissertation is to elucidate the evolutionary history of tenrecs at both deep and shallow time scales, and to use tenrecs as a proxy to understand the drivers of diversification on Madagascar. In Chapter 1 I generate the first rigorously inferred phylogeny of tenrecs to include every currently recognized species, revealing that they colonized Madagascar 30-56 million years ago. I also demonstrate that speciation rates have been higher in humid habitats compared to arid habitats - a finding that sets the groundwork for my next three chapters. To better understand the patterns and processes of diversification in the humid forest, I next explore the phylogeography of a species endemic to that region, Oryzorictes hova, in Chapter 2. Using genetic and morphometric data, I identify three populations (later identified as cryptic species) within O. hova that correspond to northern, central, and southern regions of the island. The same phylogeographic pattern has been observed in some of Madagascar's other humid-forest taxa, and it had been hypothesized that population structure is driven by low-elevation breaks between Madagascar's northern, central, and southern highlands. In Chapter 3, using genetic data from 20 small mammals and five reptiles codistributed along the island's humid-forest belt, I find this structure is directly related to the distribution of high-elevation areas and is congruent (spatially and temporally) across many species. This result demonstrates that the highlands have played an important role in recent diversification on Madagascar, most likely by functioning as refugia during Quaternary climate cycles. Finally, in Chapter 4 I continue to explore diversification in Madagascar's humid forests by studying species limits and patterns of gene flow in a clade of shrew tenrecs endemic to that region (M. fotsifotsy, M. soricoides, and M. nasoloi). Using a massively multi-locus genetic dataset, I demonstrate that M. soricoides and M. fotsifotsy (which are broadly sympatric) have hybridized in the past, and that this has caused conflicting phylogenetic results between different genetic datasets. I also recover two distinct clades of M. fotsifotsy: one that occurs only in the far north of Madagascar, and one that is widespread and broadly sympatric with M. soricoides. Evidence of reproductive isolation, plus subtle but significant morphometric differences between these clades, lead me to recognize them as distinct species. While I accomplished my primary aim of clarifying the phylogeny and taxonomy of Madagascar's tenrecs, my findings will also be important to scientists outside that initial scope. This research illuminates one of the mechanisms by which Madagascar's flora and fauna became so diverse -namely, that diversification has been driven by latitudinally segregated high-elevation refugia along the humid forests that historically spanned the island's eastern escarpment - and it reaffirms the need for continued collection and preservation of specimens in one of the world's hottest biodiversity hotspots as these forests face unprecedented rates of anthropogenic alteration.National Science Foundation Graduate Research Fellowship, Society of Systematic Biologists Graduate Student Research Award and Publisher's Award for Excellence in Systematic Research, American Society of Mammalogists Grant-in-Aid of Research and A. Brazier Howell Award, University of Alaska Fairbanks Graduate School Dissertation Completion Fellowship, National Science Foundation grant DEB-112090