Mutualism with sea anemones triggered the adaptive radiation of clownfishes.

ABSTRACT: BACKGROUND: Adaptive radiation is the process by which a single ancestral species diversifies into many descendants adapted to exploit a wide range of habitats. The appearance of ecological opportunities, or the colonisation or adaptation to novel ecological resources, has been documented to promote adaptive radiation in many classic examples. Mutualistic interactions allow species to access resources untapped by competitors, but evidence shows that the effect of mutualism on species diversification can greatly vary among mutualistic systems. Here, we test whether the development of obligate mutualism with sea anemones allowed the clownfishes to radiate adaptively across the Indian and western Pacific oceans reef habitats. RESULTS: We show that clownfishes morphological characters are linked with ecological niches associated with the sea anemones. This pattern is consistent with the ecological speciation hypothesis. Furthermore, the clownfishes show an increase in the rate of species diversification as well as rate of morphological evolution compared to their closest relatives without anemone mutualistic associations. CONCLUSIONS: The effect of mutualism on species diversification has only been studied in a limited number of groups. We present a case of adaptive radiation where mutualistic interaction is the likely key innovation, providing new insights into the mechanisms involved in the buildup of biodiversity. Due to a lack of barriers to dispersal, ecological speciation is rare in marine environments. Particular life-history characteristics of clownfishes likely reinforced reproductive isolation between populations, allowing rapid species diversification.

Adaptive learning in models with lagged variables

In this work I study the stability of the dynamics generated by adaptivelearning processes in intertemporal economies with lagged variables. Iprove that determinacy of the steady state is a necessary condition for the convergence of the learning dynamics and I show that the reciprocal is not true characterizing the economies where convergence holds. In the case of existence of cycles I show that there is not, in general, a relationship between determinacy and convergence of the learning process to the cycle. I also analyze the expectational stability of these equilibria.

An experimental study of adaptive behavior in an oligopolistic market game

We consider an oligopolistic market game, in which the players are competing firm in the same market of a homogeneous consumption good. The consumer side is represented by a fixed demand function. The firms decide how much to produce of a perishable consumption good, and they decide upon a number of information signals to be sent into the population in order to attract customers. Due to the minimal information provided, the players do not have a well--specified model of their environment. Our main objective is to characterize the adaptive behavior of the players in such a situation.

A simple adaptive procedure leading to correlated equilibrium

We propose a simple adaptive procedure for playing a game. In thisprocedure, players depart from their current play with probabilities thatare proportional to measures of regret for not having used other strategies(these measures are updated every period). It is shown that our adaptiveprocedure guaranties that with probability one, the sample distributionsof play converge to the set of correlated equilibria of the game. Tocompute these regret measures, a player needs to know his payoff functionand the history of play. We also offer a variation where every playerknows only his own realized payoff history (but not his payoff function).

Peroxisome proliferator-activated receptor alpha mediates the adaptive response to fasting.

Prolonged deprivation of food induces dramatic changes in mammalian metabolism, including the release of large amounts of fatty acids from the adipose tissue, followed by their oxidation in the liver. The nuclear receptor known as peroxisome proliferator-activated receptor alpha (PPARalpha) was found to play a role in regulating mitochondrial and peroxisomal fatty acid oxidation, suggesting that PPARalpha may be involved in the transcriptional response to fasting. To investigate this possibility, PPARalpha-null mice were subjected to a high fat diet or to fasting, and their responses were compared with those of wild-type mice. PPARalpha-null mice chronically fed a high fat diet showed a massive accumulation of lipid in their livers. A similar phenotype was noted in PPARalpha-null mice fasted for 24 hours, who also displayed severe hypoglycemia, hypoketonemia, hypothermia, and elevated plasma free fatty acid levels, indicating a dramatic inhibition of fatty acid uptake and oxidation. It is shown that to accommodate the increased requirement for hepatic fatty acid oxidation, PPARalpha mRNA is induced during fasting in wild-type mice. The data indicate that PPARalpha plays a pivotal role in the management of energy stores during fasting. By modulating gene expression, PPARalpha stimulates hepatic fatty acid oxidation to supply substrates that can be metabolized by other tissues.

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.

Rational reasoning or adaptive behavior? Evidence from two-person beauty contest games

Many experiments have shown that human subjects do not necessarily behave in line with game theoretic assumptions and solution concepts. The reasons for this non-conformity are multiple. In this paper we study the argument whether a deviation from game theory is because subjects are rational, but doubt that others are rational as well, compared to the argument that subjects, in general, are boundedly rational themselves. To distinguish these two hypotheses, we study behavior in repeated 2-person and many-person Beauty-Contest-Games which are strategically different from one another. We analyze four different treatments and observe that convergence toward equilibrium is driven by learning through the information about the other player s choice and adaptation rather than self-initiated rational reasoning.

Adaptive model selection using empirical complexities

Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

Building adaptive capacity and resilience to climate change in the water sector in Cape Verde

Purpose of the evaluation This is a scheduled standard mid-term evaluation (MTR) of a UNDP implemented GEF LDCF co-financed project. It is conducted by a team of an international and a national independent evaluator. The objective of the MTR, as set out in the Terms of Reference (TORs; Annex 1), is to provide an independent analysis of the progress of the project so far. The MTR aims to:  identify potential project design problems,  assess progress towards the achievement of the project objective and outcomes,  identify and document lessons learned (including lessons that might improve design and implementation of other projects, including UNDP-GEF supported projects), and  make recommendations regarding specific actions that should be taken to improve the project. The MTR is intended to assess signs of project success or failure and identify the necessary changes to be made. The project commenced its implementation in the first half of 2010 with the recruitment of project staff. According to the updated project plan, it is due to close in July 201410 with operations scaling down in December 2013 due to funding limits. Because of a slow implementation start, the mid-term evaluation was delayed to July 201311 The intended target audience of the evaluation are:  The project team and decision makers in the INGRH  The GEF and UNFCCC Operational Focal Points  The project partners and beneficiaries  UNDP in Cape Verde as well as the regional and headquarter (HQ) office levels  The GEF Secretariat.

Metaheuristics for the bus-driver scheduling problem

We present new metaheuristics for solving real crew scheduling problemsin a public transportation bus company. Since the crews of thesecompanies are drivers, we will designate the problem by the bus-driverscheduling problem. Crew scheduling problems are well known and severalmathematical programming based techniques have been proposed to solvethem, in particular using the set-covering formulation. However, inpractice, there exists the need for improvement in terms of computationalefficiency and capacity of solving large-scale instances. Moreover, thereal bus-driver scheduling problems that we consider can present variantaspects of the set covering, as for example a different objectivefunction, implying that alternative solutions methods have to bedeveloped. We propose metaheuristics based on the following approaches:GRASP (greedy randomized adaptive search procedure), tabu search andgenetic algorithms. These metaheuristics also present some innovationfeatures based on and genetic algorithms. These metaheuristics alsopresent some innovation features based on the structure of the crewscheduling problem, that guide the search efficiently and able them tofind good solutions. Some of these new features can also be applied inthe development of heuristics to other combinatorial optimizationproblems. A summary of computational results with real-data problems ispresented.

Decision making in uncertain and changing environments

We consider an agent who has to repeatedly make choices in an uncertainand changing environment, who has full information of the past, who discountsfuture payoffs, but who has no prior. We provide a learning algorithm thatperforms almost as well as the best of a given finite number of experts orbenchmark strategies and does so at any point in time, provided the agentis sufficiently patient. The key is to find the appropriate degree of forgettingdistant past. Standard learning algorithms that treat recent and distant pastequally do not have the sequential epsilon optimality property.

Solving a concrete sleepers production scheduling by genetic algorithms

PRECON S.A is a manufacturing company dedicated to produce prefabricatedconcrete parts to several industries as rail transportation andagricultural industries.Recently, PRECON signed a contract with RENFE,the Spanish Nnational Rail Transportation Company to manufacturepre-stressed concrete sleepers for siding of the new railways of the highspeed train AVE. The scheduling problem associated with the manufacturingprocess of the sleepers is very complex since it involves severalconstraints and objectives. The constraints are related with productioncapacity, the quantity of available moulds, satisfying demand and otheroperational constraints. The two main objectives are related withmaximizing the usage of the manufacturing resources and minimizing themoulds movements. We developed a deterministic crowding genetic algorithmfor this multiobjective problem. The algorithm has proved to be a powerfuland flexible tool to solve the large-scale instance of this complex realscheduling problem.

Scale-dependent adaptive evolution and morphological convergence to climatic niche in Californian eriogonoids (Polygonaceae)

Aim Macroevolutionary patterns and processes change substantially depending on levels of taxonomic and ecological organization, and the resolution of environmental and spatial variability. In comparative methods, the resolution of environmental and spatial variability often defines the number of selective regimes used to test whether phenotypic characteristics are adaptively correlated with the environment. Here, we examine how investigator choice of the number of selective regimes, determined by varying the resolution of among-species variability in the species climatic niche (hereafter called ecological scale'), influences trait morphological diversification among Eriogonoideae species. We assess whether adaptive or neutral processes drive the evolution of several morphological traits in these species. Location South-western North America. Methods We applied a phylogenetic framework of three evolutionary models to four morphological traits and the climatic niches of Eriogonoideae (in the buckwheat family, Polygonaceae). We tested whether morphological traits evolve in relation to climate by adaptive or neutral process, and whether the resulting patterns of morphological variability are conserved or convergent across the clade. We inspected adaptive models of evolution under different levels of resolution of among-species variability of the climatic niche. Results We show that morphological traits and climate niches of Eriogonoideae species are not phylogenetically conserved. Further, adaptive evolution of phenotypic traits is specific to climatic niche occupancy across this clade. Finally, the likely evolutionary process and the level of detectable niche conservatism change depending on the resolution of environmental variability of the climatic niche. Main conclusions Our study demonstrates the need to consider both the resolution of environmental variability and alternative evolutionary models to understand the morphological diversification that accompanies divergent adaptive evolution of lineages to climatic conditions.

Adaptive dynamics and the implementation problem with complete information

This paper studies the equilibrating process of several implementationmechanisms using naive adaptive dynamics. We show that the dynamics convergeand are stable, for the canonical mechanism of implementation in Nash equilibrium.In this way we cast some doubt on the criticism of ``complexity'' commonlyused against this mechanism. For mechanisms that use more refined equilibrium concepts,the dynamics converge but are not stable. Some papers in the literatureon implementation with refined equilibrium concepts have claimed that themechanisms they propose are ``simple'' and implement ``everything'' (incontrast with the canonical mechanism). The fact that some of these ``simple''mechanisms have unstable equilibria suggests that these statements shouldbe interpreted with some caution.