What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

The molecular mechanisms and evolutionary significance of plumage colour variation in pied flycatchers (Ficedula hypoleuca)

A common feature of natural populations is that individuals differ in morphology, physiologyand behavior (i.e .phenotype). A thorough understanding of the molecular mechanisms and evolutionary forces behind this phenotypic variation is a prerequisite for understanding evolution.This thesis examines the molecular mechanism and the roles of the different evolutionary forces in plumage colour variation in pied flycatchers (Ficedulahypoleuca). Malepied flycatchers exhibit marked variation in both pigmentary and structural plumage colourand the trait has repeatedly been suggested to be of adaptive significance. An examination of plumage colour variation on reproductive output trevealed that structural colouration, and more specifically the degree of ultraviolet (UV) reflectance had an effect on number of young sired. Paternity analyses of breeding males revealed that males that had been cuckolded by their social mate tended to be less UV reflectant than males that had not been cuckolded.Neither pigment-based norstructural colouration was found to affect the probability of siring young in other nests. Phenotypic differentiation was found to be markedly greater than differentiation at neutralgenetic markers across the pied flycatcher breeding range. Furthermore patterns of differentiationin phenotypes and selectively neutral genes were not uniform. Outlier tests searching for genomic footprints of selection revealed elevated levels of genetic divergence in a gene associated with feather development (and thus potentially structural colouration) and ultraviolet vision. Th eobserved differentiation in allelic frequencies was particularly pronounced in the Spanish piedflycatcher populations. Examining gene expression during feather development indicated that the TYRP1 gene (known to be involved in the production of black pigment) may be relevant in generating phenotypic variation in pied flycatcher plumage. Also, energy homeostasis related genesfeatured prominently among the genes found to be expressed in one extreme phenotype but not the other. This is of particular interest in light of what is known about the pleiotropy ofthe melanocortin system which underlies brown-black pigment production. The melanocortinsystem is also associated with energy homeostasis (among a number of other physiological functions) and thus the results could be pointing to the signalling function of brown-blackplumage. Plumage colour variation in pied flycatchers, both structural and pigmentary, can thus beconcluded to be exhibiting signals of non-neutral evolution. Structural colouration was found to play a role in sexual selection and putative signals of selection were further detected in acandidate gene for this trait. Evidence for non-neutral evolution of pigmentary colouration was also detected. These findings, together with the fact that preliminary evidence for an energy balance associated signalling function for plumage was found, present good starting points for further investigations into the meaning and mechanisms of plumage colour variation in piedflycatchers.

Applicabilité de la texture couleur à la différentiation des classes d’occupation du territoire sur des images satellitales multispectrales

La texture est un élément clé pour l’interprétation des images de télédétection à fine résolution spatiale. L’intégration de l’information texturale dans un processus de classification automatisée des images se fait habituellement via des images de texture, souvent créées par le calcul de matrices de co-occurrences (MCO) des niveaux de gris. Une MCO est un histogramme des fréquences d’occurrence des paires de valeurs de pixels présentes dans les fenêtres locales, associées à tous les pixels de l’image utilisée; une paire de pixels étant définie selon un pas et une orientation donnés. Les MCO permettent le calcul de plus d’une dizaine de paramètres décrivant, de diverses manières, la distribution des fréquences, créant ainsi autant d’images texturales distinctes. L’approche de mesure des textures par MCO a été appliquée principalement sur des images de télédétection monochromes (ex. images panchromatiques, images radar monofréquence et monopolarisation). En imagerie multispectrale, une unique bande spectrale, parmi celles disponibles, est habituellement choisie pour générer des images de texture. La question que nous avons posée dans cette recherche concerne justement cette utilisation restreinte de l’information texturale dans le cas des images multispectrales. En fait, l’effet visuel d’une texture est créé, non seulement par l’agencement particulier d’objets/pixels de brillance différente, mais aussi de couleur différente. Plusieurs façons sont proposées dans la littérature pour introduire cette idée de la texture à plusieurs dimensions. Parmi celles-ci, deux en particulier nous ont intéressés dans cette recherche. La première façon fait appel aux MCO calculées bande par bande spectrale et la seconde utilise les MCO généralisées impliquant deux bandes spectrales à la fois. Dans ce dernier cas, le procédé consiste en le calcul des fréquences d’occurrence des paires de valeurs dans deux bandes spectrales différentes. Cela permet, en un seul traitement, la prise en compte dans une large mesure de la « couleur » des éléments de texture. Ces deux approches font partie des techniques dites intégratives. Pour les distinguer, nous les avons appelées dans cet ouvrage respectivement « textures grises » et « textures couleurs ». Notre recherche se présente donc comme une analyse comparative des possibilités offertes par l’application de ces deux types de signatures texturales dans le cas spécifique d’une cartographie automatisée des occupations de sol à partir d’une image multispectrale. Une signature texturale d’un objet ou d’une classe d’objets, par analogie aux signatures spectrales, est constituée d’une série de paramètres de texture mesurés sur une bande spectrale à la fois (textures grises) ou une paire de bandes spectrales à la fois (textures couleurs). Cette recherche visait non seulement à comparer les deux approches intégratives, mais aussi à identifier la composition des signatures texturales des classes d’occupation du sol favorisant leur différentiation : type de paramètres de texture / taille de la fenêtre de calcul / bandes spectrales ou combinaisons de bandes spectrales. Pour ce faire, nous avons choisi un site à l’intérieur du territoire de la Communauté Métropolitaine de Montréal (Longueuil) composé d’une mosaïque d’occupations du sol, caractéristique d’une zone semi urbaine (résidentiel, industriel/commercial, boisés, agriculture, plans d’eau…). Une image du satellite SPOT-5 (4 bandes spectrales) de 10 m de résolution spatiale a été utilisée dans cette recherche. Puisqu’une infinité d’images de texture peuvent être créées en faisant varier les paramètres de calcul des MCO et afin de mieux circonscrire notre problème nous avons décidé, en tenant compte des études publiées dans ce domaine : a) de faire varier la fenêtre de calcul de 3*3 pixels à 21*21 pixels tout en fixant le pas et l’orientation pour former les paires de pixels à (1,1), c'est-à-dire à un pas d’un pixel et une orientation de 135°; b) de limiter les analyses des MCO à huit paramètres de texture (contraste, corrélation, écart-type, énergie, entropie, homogénéité, moyenne, probabilité maximale), qui sont tous calculables par la méthode rapide de Unser, une approximation des matrices de co-occurrences, c) de former les deux signatures texturales par le même nombre d’éléments choisis d’après une analyse de la séparabilité (distance de Bhattacharya) des classes d’occupation du sol; et d) d’analyser les résultats de classification (matrices de confusion, exactitudes, coefficients Kappa) par maximum de vraisemblance pour conclure sur le potentiel des deux approches intégratives; les classes d’occupation du sol à reconnaître étaient : résidentielle basse et haute densité, commerciale/industrielle, agricole, boisés, surfaces gazonnées (incluant les golfs) et plans d’eau. Nos principales conclusions sont les suivantes a) à l’exception de la probabilité maximale, tous les autres paramètres de texture sont utiles dans la formation des signatures texturales; moyenne et écart type sont les plus utiles dans la formation des textures grises tandis que contraste et corrélation, dans le cas des textures couleurs, b) l’exactitude globale de la classification atteint un score acceptable (85%) seulement dans le cas des signatures texturales couleurs; c’est une amélioration importante par rapport aux classifications basées uniquement sur les signatures spectrales des classes d’occupation du sol dont le score est souvent situé aux alentours de 75%; ce score est atteint avec des fenêtres de calcul aux alentours de11*11 à 15*15 pixels; c) Les signatures texturales couleurs offrant des scores supérieurs à ceux obtenus avec les signatures grises de 5% à 10%; et ce avec des petites fenêtres de calcul (5*5, 7*7 et occasionnellement 9*9) d) Pour plusieurs classes d’occupation du sol prises individuellement, l’exactitude dépasse les 90% pour les deux types de signatures texturales; e) une seule classe est mieux séparable du reste par les textures grises, celle de l’agricole; f) les classes créant beaucoup de confusions, ce qui explique en grande partie le score global de la classification de 85%, sont les deux classes du résidentiel (haute et basse densité). En conclusion, nous pouvons dire que l’approche intégrative par textures couleurs d’une image multispectrale de 10 m de résolution spatiale offre un plus grand potentiel pour la cartographie des occupations du sol que l’approche intégrative par textures grises. Pour plusieurs classes d’occupations du sol un gain appréciable en temps de calcul des paramètres de texture peut être obtenu par l’utilisation des petites fenêtres de traitement. Des améliorations importantes sont escomptées pour atteindre des exactitudes de classification de 90% et plus par l’utilisation des fenêtres de calcul de taille variable adaptées à chaque type d’occupation du sol. Une méthode de classification hiérarchique pourrait être alors utilisée afin de séparer les classes recherchées une à la fois par rapport au reste au lieu d’une classification globale où l’intégration des paramètres calculés avec des fenêtres de taille variable conduirait inévitablement à des confusions entre classes.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

On the Computation of Fourier Coefficients

In this paper we derive an identity for the Fourier coefficients of a differentiable function f(t) in terms of the Fourier coefficients of its derivative f'(t). This yields an algorithm to compute the Fourier coefficients of f(t) whenever the Fourier coefficients of f'(t) are known, and vice versa. Furthermore this generates an iterative scheme for N times differentiable functions complementing the direct computation of Fourier coefficients via the defining integrals which can be also treated automatically in certain cases.

Künstliche Randbedingungen und Algebraische Äquivalenzen in der Elastizitätstheorie

In dieser Arbeit werden zwei Aspekte bei Randwertproblemen der linearen Elastizitätstheorie untersucht: die Approximation von Lösungen auf unbeschränkten Gebieten und die Änderung von Symmetrieklassen unter speziellen Transformationen. Ausgangspunkt der Dissertation ist das von Specovius-Neugebauer und Nazarov in "Artificial boundary conditions for Petrovsky systems of second order in exterior domains and in other domains of conical type"(Math. Meth. Appl. Sci, 2004; 27) eingeführte Verfahren zur Untersuchung von Petrovsky-Systemen zweiter Ordnung in Außenraumgebieten und Gebieten mit konischen Ausgängen mit Hilfe der Methode der künstlichen Randbedingungen. Dabei werden für die Ermittlung von Lösungen der Randwertprobleme die unbeschränkten Gebiete durch das Abschneiden mit einer Kugel beschränkt, und es wird eine künstliche Randbedingung konstruiert, um die Lösung des Problems möglichst gut zu approximieren. Das Verfahren wird dahingehend verändert, dass das abschneidende Gebiet ein Polyeder ist, da es für die Lösung des Approximationsproblems mit üblichen Finite-Element-Diskretisierungen von Vorteil sei, wenn das zu triangulierende Gebiet einen polygonalen Rand besitzt. Zu Beginn der Arbeit werden die wichtigsten funktionalanalytischen Begriffe und Ergebnisse der Theorie elliptischer Differentialoperatoren vorgestellt. Danach folgt der Hauptteil der Arbeit, der sich in drei Bereiche untergliedert. Als erstes wird für abschneidende Polyedergebiete eine formale Konstruktion der künstlichen Randbedingungen angegeben. Danach folgt der Nachweis der Existenz und Eindeutigkeit der Lösung des approximativen Randwertproblems auf dem abgeschnittenen Gebiet und im Anschluss wird eine Abschätzung für den resultierenden Abschneidefehler geliefert. An die theoretischen Ausführungen schließt sich die Betrachtung von Anwendungsbereiche an. Hier werden ebene Rissprobleme und Polarisationsmatrizen dreidimensionaler Außenraumprobleme der Elastizitätstheorie erläutert. Der letzte Abschnitt behandelt den zweiten Aspekt der Arbeit, den Bereich der Algebraischen Äquivalenzen. Hier geht es um die Transformation von Symmetrieklassen, um die Kenntnis der Fundamentallösung der Elastizitätsprobleme für transversalisotrope Medien auch für Medien zu nutzen, die nicht von transversalisotroper Struktur sind. Eine allgemeine Darstellung aller Klassen konnte hier nicht geliefert werden. Als Beispiel für das Vorgehen wird eine Klasse von orthotropen Medien im dreidimensionalen Fall angegeben, die sich auf den Fall der Transversalisotropie reduzieren lässt.

Computation of relativistic symmetry orbitals for finite double point groups

A program is presented for the construction of relativistic symmetry-adapted molecular basis functions. It is applicable to 36 finite double point groups. The algorithm, based on the projection operator method, automatically generates linearly independent basis sets. Time reversal invariance is included in the program, leading to additional selection rules in the non-relativistic limit.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Ein Algorithmus zur numerischen Verifikation der äquivarianten Tamagawazahlvermutung für eine Familie von Zahlkörpererweiterungen

Sei $N/K$ eine galoissche Zahlkörpererweiterung mit Galoisgruppe $G$, so dass es in $N$ eine Stelle mit voller Zerlegungsgruppe gibt. Die vorliegende Arbeit beschäftigt sich mit Algorithmen, die für das gegebene Fallbeispiel $N/K$, die äquivariante Tamagawazahlvermutung von Burns und Flach für das Paar $(h^0(Spec(N), \mathbb{Z}[G]))$ (numerisch) verifizieren. Grob gesprochen stellt die äquivariante Tamagawazahlvermutung (im Folgenden ETNC) in diesem Spezialfall einen Zusammenhang her zwischen Werten von Artinschen $L$-Reihen zu den absolut irreduziblen Charakteren von $G$ und einer Eulercharakteristik, die man in diesem Fall mit Hilfe einer sogenannten Tatesequenz konstruieren kann. Unter den Voraussetzungen 1. es gibt eine Stelle $v$ von $N$ mit voller Zerlegungsgruppe, 2. jeder irreduzible Charakter $\chi$ von $G$ erfüllt eine der folgenden Bedingungen 2a) $\chi$ ist abelsch, 2b) $\chi(G) \subset \mathbb{Q}$ und $\chi$ ist eine ganzzahlige Linearkombination von induzierten trivialen Charakteren; wird ein Algorithmus entwickelt, der ETNC für jedes Fallbeispiel $N/\mathbb{Q}$ vollständig beweist. Voraussetzung 1. erlaubt es eine Idee von Chinburg ([Chi89]) umzusetzen zur algorithmischen Berechnung von Tatesequenzen. Dabei war es u.a. auch notwendig lokale Fundamentalklassen zu berechnen. Im höchsten zahm verzweigten Fall haben wir hierfür einen Algorithmus entwickelt, der ebenfalls auf den Ideen von Chinburg ([Chi85]) beruht, die auf Arbeiten von Serre [Ser] zurück gehen. Für nicht zahm verzweigte Erweiterungen benutzen wir den von Debeerst ([Deb11]) entwickelten Algorithmus, der ebenfalls auf Serre's Arbeiten beruht. Voraussetzung 2. wird benötigt, um Quotienten aus den $L$-Werten und Regulatoren exakt zu berechnen. Dies gelingt, da wir im Fall von abelschen Charakteren auf die Theorie der zyklotomischen Einheiten zurückgreifen können und im Fall (b) auf die analytische Klassenzahlformel von Zwischenkörpern. Ohne die Voraussetzung 2. liefern die Algorithmen für jedes Fallbeispiel $N/K$ immer noch eine numerische Verifikation bis auf Rechengenauigkeit. Den Algorithmus zur numerischen Verifikation haben wir für $A_4$-Erweiterungen über $\mathbb{Q}$ in das Computeralgebrasystem MAGMA implementiert und für 27 Erweiterungen die äquivariante Tamagawazahlvermutung numerisch verifiziert.

Measure Fields for Function Approximation

The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components.

Priors Stabilizers and Basis Functions: From Regularization to Radial, Tensor and Additive Splines

We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this paper we show that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models, Breiman's hinge functions and some forms of Projection Pursuit Regression. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In the final part of the paper, we also show a relation between activation functions of the Gaussian and sigmoidal type.

A compositional data analysis package for R providing multiple approaches

”compositions” is a new R-package for the analysis of compositional and positive data. It contains four classes corresponding to the four different types of compositional and positive geometry (including the Aitchison geometry). It provides means for computation, plotting and high-level multivariate statistical analysis in all four geometries. These geometries are treated in an fully analogous way, based on the principle of working in coordinates, and the object-oriented programming paradigm of R. In this way, called functions automatically select the most appropriate type of analysis as a function of the geometry. The graphical capabilities include ternary diagrams and tetrahedrons, various compositional plots (boxplots, barplots, piecharts) and extensive graphical tools for principal components. Afterwards, ortion and proportion lines, straight lines and ellipses in all geometries can be added to plots. The package is accompanied by a hands-on-introduction, documentation for every function, demos of the graphical capabilities and plenty of usage examples. It allows direct and parallel computation in all four vector spaces and provides the beginner with a copy-and-paste style of data analysis, while letting advanced users keep the functionality and customizability they demand of R, as well as all necessary tools to add own analysis routines. A complete example is included in the appendix

Valores culturais, orientações políticas e classes sociais : consequências nos valores de trabalho

Aborda-se a temática das estruturas de valores e consequente impacto nas atitudes e comportamentos na sociedade em geral e no trabalho em particular. Uma amostra diversificada de 157 participantes, recrutada em diferentes meios sociais, variando assim em termos etários, de qualificações académicas, de classe social e de simpatia partidária, respondeu a um questionário sobre valores, valores de trabalho e orientações políticas. Primeiro, verificámos que as orientações político-ideológicas são estruturadas em duas dimensões correlacionadas mas independentes: a esquerda-direita e o autoritarismo-liberdade. Também verificámos que diferentes dimensões supraordenadas de valores do modelo de valores universais de Schwartz estruturam os valores de trabalho e as orientações político-ideológicas, com impactos específicos sobre as funções laborais desejáveis e as preferências socioeconómico-culturais. Finalmente, os efeitos das dimensões de classe social (detenção de capital e qualificação académica) sobre as orientações político-ideológicas sugerem que as dimensões direita-esquerda e autoritarismo-liberdade, apesar de estarem correlacionadas, têm motivações e origens sociológicas diferentes.

Managing threatened species – the ecological toolbox, evolutionary theory and the declining-population paradigm

1. The management of threatened species is an important practical way in which conservationists can intervene in the extinction process and reduce the loss of biodiversity. Understanding the causes of population declines (past, present and future) is pivotal to designing effective practical management. This is the declining-population paradigm identified by Caughley. 2. There are three broad classes of ecological tool used by conservationists to guide management decisions for threatened species: statistical models of habitat use, demographic models and behaviour-based models. Each of these is described here, illustrated with a case study and evaluated critically in terms of its practical application. 3. These tools are fundamentally different. Statistical models of habitat use and demographic models both use descriptions of patterns in abundance and demography, in relation to a range of factors, to inform management decisions. In contrast, behaviourbased models describe the evolutionary processes underlying these patterns, and derive such patterns from the strategies employed by individuals when competing for resources under a specific set of environmental conditions. 4. Statistical models of habitat use and demographic models have been used successfully to make management recommendations for declining populations. To do this, assumptions are made about population growth or vital rates that will apply when environmental conditions are restored, based on either past data collected under favourable environmental conditions or estimates of these parameters when the agent of decline is removed. As a result, they can only be used to make reliable quantitative predictions about future environments when a comparable environment has been experienced by the population of interest in the past. 5. Many future changes in the environment driven by management will not have been experienced by a population in the past. Under these circumstances, vital rates and their relationship with population density will change in the future in a way that is not predictable from past patterns. Reliable quantitative predictions about population-level responses then need to be based on an explicit consideration of the evolutionary processes operating at the individual level. 6. Synthesis and applications. It is argued that evolutionary theory underpins Caughley’s declining-population paradigm, and that it needs to become much more widely used within mainstream conservation biology. This will help conservationists examine critically the reliability of the tools they have traditionally used to aid management decision-making. It will also give them access to alternative tools, particularly when predictions are required for changes in the environment that have not been experienced by a population in the past.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC.