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Models relating phenotype space to fitness (phenotype-fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher's geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model "from first principles" is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher's model's assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts. +
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Niche partitioning due to adaptive foraging reverses effects of nestedness and connectance on pollination network stability +
Much research debates whether properties of ecological networks such as nestedness and con- nectance stabilise biological communities while ignoring key behavioural aspects of organisms within these networks. Here, we computationally assess how adaptive foraging (AF) behaviour interacts with network architecture to determine the stability of plant–pollinator networks. We find that AF reverses negative effects of nestedness and positive effects of connectance on the sta- bility of the networks by partitioning the niches among species within guilds. This behaviour enables generalist pollinators to preferentially forage on the most specialised of their plant part- ners which increases the pollination services to specialist plants and cedes the resources of general- ist plants to specialist pollinators. We corroborate these behavioural preferences with intensive field observations of bee foraging. Our results show that incorporating key organismal behaviours with well-known biological mechanisms such as consumer-resource interactions into the analysis of ecological networks may greatly improve our understanding of complex ecosystems. +
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Phenotypic variation is common in most pathogens, yet the mechanisms that maintain this diver- sity are still poorly understood. We asked whether continuous host variation in susceptibility helps maintain phenotypic variation, using experiments conducted with a baculovirus that infects gypsy moth (Lymantria dispar) larvae. We found that an empirically observed tradeoff between mean transmission rate and variation in transmission, which results from host heterogeneity, promotes long-term coexistence of two pathogen types in simulations of a population model. This tradeoff introduces an alternative strategy for the pathogen: a low-transmission, low-variability type can coexist with the high-transmission type favoured by classical non-heterogeneity models. In addi- tion, this tradeoff can help explain the extensive phenotypic variation we observed in field-col- lected pathogen isolates, in traits affecting virus fitness including transmission and environmental persistence. Similar heterogeneity tradeoffs might be a general mechanism promoting phenotypic variation in any pathogen for which hosts vary continuously in susceptibility. +
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Phytoplankton are key components of aquatic ecosystems, fixing CO2 from the atmosphere through photosynthesis and supporting secondary production, yet relatively little is known about how future global warming might alter their biodiversity and associated ecosystem functioning. Here, we explore how the structure, function, and biodiversity of a planktonic metacommunity was altered after five years of experimental warming. Our outdoor mesocosm experiment was open to natural dispersal from the regional species pool, allowing us to explore the effects of experimental warming in the context of metacommunity dynamics. Warming of 4°C led to a 67% increase in the species richness of the phytoplankton, more evenly-distributed abundance, and higher rates of gross primary productivity. Warming elevated productivity indirectly, by increasing the biodiversity and biomass of the local phytoplankton communities. Warming also systematically shifted the taxonomic and functional trait composition of the phytoplankton, favoring large, colonial, inedible phytoplankton taxa, suggesting stronger top-down control, mediated by zooplankton grazing played an important role. Overall, our findings suggest that temperature can modulate species coexistence, and through such mechanisms, global warming could, in some cases, increase the species richness and productivity of phytoplankton communities. +
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Pollination systems are recognized as critical for the maintenance of biodiversity in terrestrial ecosystems. Therefore, the understanding of mechanisms that promote the integrity of those mutualistic assemblages is an important issue for the conservation of biodiversity and ecosystem function. In this study we present a new population dynamics model for plant–pollinator interactions that is based on the consumer–resource approach and incorporates a few essential features of pollination ecology. The model was used to project the temporal dynamics of three empirical pollination network, in order to analyze how adaptive foraging of pollinators (AF) shapes the outcome of community dynamics in terms of biodiversity and network robustness to species loss. We found that the incorporation of AF into the dynamics of the pollination networks increased the persistence and diversity of its constituent species, and reduced secondary extinctions of both plants and animals. These findings were best explained by the following underlying processes: 1) AF increased the amount of floral resources extracted by specialist pollinators, and 2) AF raised the visitation rates received by specialist plants. We propose that the main mechanism by which AF enhanced those processes is (trophic) niche partitioning among animals, which in turn generates (pollen vector) niche partitioning among plants. Our results suggest that pollination networks can maintain their stability and diversity by the adaptive foraging of generalist pollinators. +
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Prediction of post-vaccine population structure of Streptococcus pneumoniae using accessory gene frequencies +
Predictions of how a population will respond to a selective pressure are valuable, especially in the case of infectious diseases, which often adapt to the interventions we use to control them. Yet attempts to predict how pathogen populations will change, for example in response to vaccines, are challenging. Such has been the case with Streptococcus pneumoniae , an important human colonizer and pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use recent advances in knowledge of negative-frequency dependent selection (NFDS) acting on frequencies of accessory genes (i.e., flexible genome) to predict the changes in the pneumococcal population after intervention. Implementing a deterministic NFDS model using the replicator equation, we can accurately predict which pneumococcal lineages will increase after intervention. Analyzing a population genomic sample of pneumococci collected before and after vaccination, we find that the predicted fitness of a lineage post-vaccine is significantly and positively correlated with the observed change in its prevalence. Then, using quadratic programming to numerically solve the frequencies of non-vaccine type lineages that best restored the pre-vaccine accessory gene frequencies, we accurately predict the post-vaccine population composition. Additionally, we also test the predictive ability of frequencies of core genome loci, a subset of metabolic loci, and naive estimates of prevalence change based on pre-vaccine lineages frequencies. Finally, we show how this approach can assess the migration and invasion capacity of emerging lineages, on the basis of their accessory genome. In general, we provide a method for predicting the impact of an intervention on pneumococcal populations and other bacterial pathogens for which NFDS is a main driving force. +
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Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters. +
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Recent results have shown that deep neural networks (DNN) may have significant potential to serve as quantitatively precise models of sensory cortex neural populations. However, the implications these results have for our conceptual understanding of neural mechanisms are subtle. This is because many modern DNN brain models are best understood as the products of task-constrained optimization processes, unlike the intuitively simpler hand-crafted models from earlier approaches. In this chapter, we illustrate these issues by first discussing the nature of information processing in the primate ventral visual pathway, and review results comparing the response properties of units in goal-optimized DNN models to neural responses found throughout the ventral pathway. We then show how DNN visual system models are just one instance of a more general optimization framework whose logic may be applicable to understanding the underlying constraints that shape neural mechanisms throughout the brain.
An important part of a scientist's job is to answer "why" questions. For cognitive neuroscientists, a core objective is to uncover the underlying reasons why the structures of the human brain are as they are. Since brains are biological systems, answering such questions is ultimately a matter of identifying the evolutionary and developmental constraints that shape brain structure and function. Such constraints are in part architectural: what large-scale brain structures are put in place genetically to help a brain help its host organism better meet evolutionary challenges? In light of the centrality of behavior in understanding the brain, an ethological investigation is also indicated: what behavioral goals most strongly constrain a given neural system? And since many complex behaviors in higher organisms are not entirely genetically determined and must instead be partly derived through experience of the world, a core question of learning is also involved: how do learning rules that absorb experiential data constrain what brains look like?
The interactions between architectural structure, behavioral goals, and learning rules suggest a quantitative optimization framework as one route toward answering these "why" questions. Put simply, this means: postulating one or several goal behavior(s) as driving the evolution and/or development of a neural system of interest; finding architecturally plausible computational models that (attempt to) optimize for the behavior; and then quantitatively comparing the internal structures arrived at in the optimized models to measurements
from large-scale neuroscience experiments. To the extent that there is a match between optimized models and the real data that is very substantially better than that found for various controls (e.g. models designed by hand or optimized for other tasks), this is evidence that something important has been understood about the underlying constraints that shape the brain system under investigation. Though it might sound challenging to
put this approach into practice, recent successes suggest we might add to our list of maxims the observation that nothing in computational cognitive neuroscience makes sense except in light of optimization.
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The maps problem and the mapping problem: Two challenges for a cognitive neuroscience of speech and language +
Research on the brain basis of speech and language faces theoretical and empirical challenges. Most current research, dominated by imaging, deficit-lesion, and electrophysiological techniques, seeks to identify regions that underpin aspects of language processing such as phonology, syntax, or semantics. The emphasis lies on localization and spatial characterization of function. The first part of the paper deals with a practical challenge that arises in the context of such a research programme. This maps problem concerns the extent to which spatial information and localization can satisfy the explanatory needs for perception and cognition. Several areas of investigation exemplify how the neural basis of speech and language is discussed in those terms (regions, streams, hemispheres, networks). The second part of the paper turns to a more troublesome challenge, namely how to formulate the formal links between neurobiology and cognition. This principled problem thus addresses the relation between the primitives of cognition (here speech, language) and neurobiology. Dealing with this mapping problem invites the development of linking hypotheses between the domains. The cognitive sciences provide granular, theoretically motivated claims about the structure of various domains (the "cognome"); neurobiology, similarly, provides a list of the available neural structures. However, explanatory connections will require crafting of computationally explicit linking hypotheses at the right level of abstraction. For both the practical maps problem and the principled mapping problem, developmental approaches and evidence can play a central role in the resolution. © 2012 Copyright Psychology Press. +
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Sleep is crucial for daytime functioning, cognitive performance and general well-being. These aspects of daily life are known to be impaired after extended wake, yet, the underlying neuronal correlates have been difficult to identify. Accumulating evidence suggests that normal functioning of the brain is characterized by long-range temporal correlations (LRTCs) in cortex, which are supportive for decision-making and working memory tasks. Here we assess LRTCs in resting state human EEG data during a 40-hour sleep deprivation experiment by evaluating the decay in autocorrelation and the scaling exponent of the detrended fluctuation analysis from EEG amplitude fluctuations. We find with both measures that LRTCs decline as sleep deprivation progresses. This decline becomes evident when taking changes in signal power into appropriate consideration. Our results demonstrate the importance of sleep to maintain LRTCs in the human brain. In complex networks, LRTCs naturally emerge in the vicinity of a critical state. The observation of declining LRTCs during wake thus provides additional support for our hypothesis that sleep reorganizes cortical networks towards critical dynamics for optimal functioning. +
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Starting with Darwin, biologists have asked how populations evolve from a low fitness state that is evolutionarily stable to a high fitness state that is not. Specifically of interest is the emergence of cooperation and multicellularity where the fitness of individuals often appears in conflict with that of the population. Theories of social evolution and evolutionary game theory have produced a number of fruitful results employing two-state two-body frameworks. In this study, we depart from this tradition and instead consider a multi-player, multi-state evolutionary game, in which the fitness of an agent is determined by its relationship to an arbitrary number of other agents. We show that populations organize themselves in one of four distinct phases of interdependence depending on one parameter, selection strength. Some of these phases involve the formation of specialized large-scale structures. We then describe how the evolution of independence can be manipulated through various external perturbations. +
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Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin, and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics, and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analyzed the largest cohort and set of distinct, clinically relevant body habitats to date. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families, and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associationa of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology, and translational applications of the human microbiome. +
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The Utility of Fisher's Geometric Model in Evolutionary Genetics Phenotypic complexity: the number of statistically independent phenotypic traits an organism exposes to natural selection in a given environment +
The accumulation of data on the genomic bases of adaptation has triggered renewed interest in theoretical models of adaptation. Among these models, Fisher's geometric model (FGM) has received a lot of attention over the past two decades. FGM is based on a continuous multidimensional phenotypic landscape, but it is mostly used for the emerging properties of individual mutation effects. Despite its apparent simplicity and limited number of pa-rameters, FGM integrates a full model of mutation and epistatic interactions that allows the study of both beneficial and deleterious mutations and, subse-quently, the fate of evolving populations. In this review, I present the different properties of FGM and the qualitative and quantitative support they have received from experimental evolution data. I then discuss how to estimate the different parameters of the model and outline some future directions to connect FGM and the molecular determinants of adaptation. +
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The brain comprises complex structural and functional networks, but much remains to be determined regarding how these networks support the communication processes that underlie neuronal computation. In this Review, Avena-Koenigsberger, Misic and Sporns discuss the network basis of communication dynamics in the brain. +
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Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life +
The diversification of life involved enormous increases in size and complexity. The evolutionary transitions from prokaryotes to unicellular eukaryotes to metazoans were accompanied by major innovations in metabolic design. Here we show that the scalings of metabolic rate, population growth rate, and production efficiency with body size have changed across the evolutionary transitions. Metabolic rate scales with body mass superlinearly in prokaryotes, linearly in protists, and sublinearly in metazoans, so Kleiber's 3/4 power scaling law does not apply universally across organisms. The scaling of maximum population growth rate shifts from positive in prokaryotes to negative in protists and metazoans, and the efficiency of production declines across these groups. Major changes in metabolic processes during the early evolution of life overcame existing constraints, exploited new opportunities, and imposed new constraints. +
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The geometric mean of fitness is considered to be the main indicator of evolutionary change in stochastic models. However, this measure was initially derived for models with infinite population sizes, where the long-term evolutionary behavior can be described with certainty. In this paper we begin an exploration of the limitations and utility of this approach to evolution in finite populations and discuss alternate methods for predicting evolutionary dynamics. We reanalyze a model of lottery competition under environmental stochasticity by including population finiteness, and show that the geometric mean predictions do not always agree with those based on the fixa- tion probability of rare alleles. Further, the fixation probability can be inserted into adaptive dynamics equations to derive the mean state of the population. We explore the effects of increasing population size on these conclusions through simulations. These simulations show that for small population sizes the fixation probability accurately predicts the course of evolution, but as population size becomes large the geometric mean predictions are upheld. The two approaches are reconciled because the time scale on which the fixation probability approach applies becomes very large as population size grows. +
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Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics +
The interspecies exchange of metabolites plays a key role in the spatiotemporal dynamics of microbial communities. This raises the question of whether ecosystem-level behavior of structured communities can be predicted using genome-scale metabolic models for multiple organisms. We developed a modeling framework that integrates dynamic flux balance analysis with diffusion on a lattice and applied it to engineered communities. First, we predicted and experimentally confirmed the species ratio to which a two-species mutualistic consortium converges and the equilibrium composition of a newly engineered three-member community. We next identified a specific spatial arrangement of colonies, which gives rise to what we term the "eclipse dilemma": does a competitor placed between a colony and its cross-feeding partner benefit or hurt growth of the original colony? Our experimentally validated finding that the net outcome is beneficial highlights the complex nature of metabolic interactions in microbial communities while at the same time demonstrating their predictability. © 2014 The Authors. +
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The meaning of language is represented in regions of the cerebral cortex collectively known as the ‘semantic system’. However, little of the semantic system has been mapped comprehensively, and the semantic selectivity of most regions is unknown. Here we systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI (fMRI) data collected while subjects listened to hours of narrative stories. We show that the semantic system is organized into intricate patterns that seem to be consistent across individuals. We then use a novel generative model to create a detailed semantic atlas. Our results suggest that most areas within the semantic system represent information about specific semantic domains, or groups of related concepts, and our atlas shows which domains are represented in each area. This study demonstrates that data-driven methods—commonplace in studies of human neuroanatomy and functional connectivity—provide a powerful and efficient means for mapping functional representations in the brain. +
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Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation +
The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine. Gratton et al. comprehensively measure individual, day-to-day, and task variance in functional brain networks, revealing that networks are dominated by stable individual factors, not cognitive content. These findings suggest utility of functional network measurements in personalized medicine. +
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The past twenty years have seen a resurgence of interest in nonequilibrium thermodynamics, thanks to advances in the theory of stochastic processes and in their thermodynamic interpretation. Fluctuation theorems provide fundamental constraints on the dynamics of systems arbitrarily far from thermal equilibrium. Thermodynamic uncertainty relations bound the dissipative cost of precision in a wide variety of processes. Concepts of excess work and excess heat provide the basis for a complete thermodynamics of nonequilibrium steady states, including generalized Clausius relations and thermodynamic potentials. But these general results carry their own limitations: fluctuation theorems involve exponential averages that can depend sensitively on unobservably rare trajectories; steady-state thermodynamics makes use of a dual dynamics that lacks any direct physical interpretation. This review aims to present these central results of contemporary nonequilibrium thermodynamics in such a way that the power of each claim for making physical predictions can be clearly assessed, using examples from current topics in soft matter and biophysics. +
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The primary somatosensory (S1) and motor (M1) cortices are somatotopically organized, with distinct brain areas receiving information from—and controlling the movements of specific body parts. But once this specialized organization has formed, can it be altered to support the changing needs of the organism? In this chapter, we reexamine the extent to which reorganization of the cortical hand representation occurs in the adult primate brain, with respect to both physiology and behavior. We review seminal findings reporting changes in the cortical maps of humans and nonhuman primates, which have been interpreted as evidence of brain reorganization: a qualitative change in the response of neurons. For example, a hand sensory area now responds to tactile stimulation of the face. We focus on three potential triggers for brain reorganization: changed input (due to training), input loss (due to amputation), and substrate loss (due to stroke). We review recent research demonstrating that the canonical functional organization in S1 and M1 is more resilient than originally assumed. Instead, experience or injury-dependent cortical map changes more likely result either from the unmasking of preexisting cortical connections or from subcortical reorganization, rather than true cortical reorganization. Finally, we examine whether cortical map changes, whatever their physiological origin, have any causal adaptive or maladaptive consequence on perception and action and conclude that they do not. Overall, our review suggests that although the adult brain can change based on experience or injury, cortical reorganization does not need to be invoked as the driving mechanism. +
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The purpose of this paper is to describe a framework for the understanding of rules that govern how neural system dynamics are coordinated to produce behavior. The framework, structured flows on manifolds (SFM), posits that neural processes are flows depicting system interactions that occur on relatively low-dimension manifolds, which constrain possible functional configurations. Although this is a general framework, we focus on the application to brain disorders. We first explain the Epileptor, a phenomenological computational model showing fast and slow dynamics, but also a hidden repertoire whose expression is similar to refractory status epilepticus. We suggest that epilepsy represents an innate brain state whose potential may be realized only under certain circumstances. Conversely, deficits from damage or disease processes, such as stroke or dementia, may reflect both the disease process per se and the adaptation of the brain. SFM uniquely captures both scenarios. +
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Differential and enhanced response to climate forcing in diarrheal disease due to rotavirus across a megacity of the developing world +
The role of climate forcing in the population dynamics of infectious diseases has typically been revealed via retrospective analyses of incidence records aggregated across space and, in particular, over whole cities. Here, we focus on the transmission dynamics of rotavirus, the main diarrheal disease in infants and young children, within the megacity of Dhaka, Bangladesh. We identify two zones, the densely urbanized core and the more rural periphery, that respond differentially to flooding. Moreover, disease seasonality differs substantially between these regions, spanning variation comparable to the variation from tropical to temperate regions. By combining process-based models with an extensive disease surveillance record, we show that the response to climate forcing is mainly seasonal in the core, where a more endemic transmission resulting from an asymptomatic reservoir facilitates the response to the monsoons. The force of infection in this monsoon peak can be an order of magnitude larger than the force of infection in the more epidemic periphery, which exhibits little or no postmonsoon outbreak in a pattern typical of nearby rural areas. A typically smaller peak during the monsoon season nevertheless shows sensitivity to interannual variability in flooding. High human density in the core is one explanation for enhanced transmission during troughs and an associated seasonal monsoon response in this diarrheal disease, which unlike cholera, has not been widely viewed as climate-sensitive. Spatial demographic, socioeconomic, and environmental heterogeneity can create reservoirs of infection and enhance the sensitivity of disease systems to climate forcing, especially in the populated cities of the developing world. +
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The underlying cause of aging remains one of the central mysteries of biology. Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues. This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming. By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges. +
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There is growing concern over tipping points arising in ecosystems due to the crossing of environmental thresholds. Tipping points lead to strong and possibly irreversible shifts between alternative ecosystem states incurring high societal costs. Traits are central to the feedbacks that maintain alternative ecosystem states, as they govern the responses of populations to environmental change that could stabilize or destabilize ecosystem states. However, we know little about how evolutionary changes in trait distributions over time affect the occurrence of tipping points, and even less about how big scale ecological shifts reciprocally interact with trait dynamics. We argue that interactions between ecological and evolutionary processes should be taken into account for understanding the balance of feedbacks governing tipping points in nature. +
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Thermodynamics is the paradigm example in physics of a time-asymmetric theory, but the origin of the asymmetry lies deeper than the second law. A primordial arrow can be defined by the way of the equilibration principle (“minus first law”). By appealing to this arrow, the nature of the well-known ambiguity in Carathéodory's 1909 version of the second law becomes clear. Following Carathéodory's seminal work, formulations of thermodynamics have gained ground that highlight the role of the binary relation of adiabatic accessibility between equilibrium states, the most prominent recent example being the important 1999 axiomatization due to Lieb and Yngvason. This formulation can be shown to contain an ambiguity strictly analogous to that in Carathéodory's treatment. +
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This paper explains some implications of Markov-process theory for models of mortality. We show that an important qualitative feature common to empirical mortality data, and which has been found in certain models - the convergence to a "mortality plateau" - is, in fact, a generic consequence of the models' convergence to a "quasistationary distribution". This phenomenon has been explored extensively in the mathematical literature. Not only does this generalization free important results from specifics of the models, it also suggests a new explanation for the convergence to constant mortality. At the same time that we show that the late behavior of these models - convergence to a finite asymptote - is almost logically immutable, we also point out that the early behavior of the mortality rates can be more flexible than has been generally acknowledged. We show, in particular, that an appropriate choice of initial conditions enables one popular model to approximate any reasonable hazard-rate data. This illustrates how precarious it can be to read a model's vindication from its consilience with a favored hazard-rate function, such as the Gompertz exponential. © 2004 Elsevier Inc. All rights reserved. +
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This study sought to examine the effect of meditation experience on brain networks underlying cognitive actions employed during contemplative practice. In a previous study, we proposed a basic model of naturalistic cognitive fluctuations that occur during the practice of focused attention meditation. This model specifies four intervals in a cognitive cycle: mind wandering (MW), awareness of MW, shifting of attention, and sustained attention. Using subjective input from experienced practitioners during meditation, we identified activity in salience network regions during awareness of MW and executive network regions during shifting and sustained attention. Brain regions associated with the default mode were active during MW. In the present study, we reasoned that repeated activation of attentional brain networks over years of practice may induce lasting functional connectivity changes within relevant circuits. To investigate this possibility, we created seeds representing the networks that were active during the four phases of the earlier study, and examined functional connectivity during the resting state in the same participants. Connectivity maps were then contrasted between participants with high vs. low meditation experience. Participants with more meditation experience exhibited increased connectivity within attentional networks, as well as between attentional regions and medial frontal regions. These neural relationships may be involved in the development of cognitive skills, such as maintaining attention and disengaging from distraction, that are often reported with meditation practice. Furthermore, because altered connectivity of brain regions in experienced meditators was observed in a non-meditative (resting) state, this may represent a transference of cognitive abilities off the cushion into daily life. +
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To explain diversity in forests, niche theory must show how multiple plant species coexist while competing for the same resources. Although successional processes are widespread in forests, theoretical work has suggested that differentiation in successional strategy allows only a few species stably to coexist, including only a single shade tolerant. However, this conclusion is based on current niche models, which encode a very simplified view of plant communities, suggesting that the potential for niche differentiation has remained unexplored. Here, we show how extending successional niche models to include features common to all vegetation-height-structured competition for light under a prevailing disturbance regime and two trait-mediated tradeoffs in plant function-enhances the diversity of species that can be maintained, including a diversity of shade tolerants. We identify two distinct axes of potential niche differentiation, corresponding to the traits leaf mass per unit leaf area and height at maturation. The first axis allows for coexistence of different shade tolerances and the second axis for coexistence among species with the same shade tolerance. Addition of this second axis leads to communities with a high diversity of shade tolerants. Niche differentiation along the second axis also generates regions of trait space wherein fitness is almost equalized, an outcome we term "evolutionarily emergent near-neutrality." For different environmental conditions, our model predicts diverse vegetation types and trait mixtures, akin to observations. These results indicate that the outcomes of successional niche differentiation are richer than previously thought and potentially account for mixtures of traits and species observed in forests worldwide. +
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To understand the effects of temperature on biological systems, we compile, organize, and analyze a database of 1,072 thermal responses for microbes, plants, and animals. The unprecedented diversity of traits (n = 112), species (n = 309), body sizes (15 orders of magnitude), and habitats (all major biomes) in our database allows us to quantify novel features of the temperature response of biological traits. In particular, analysis of the rising component of within-species (intraspecific) responses reveals that 87% are fit well by the Boltzmann-Arrhenius model. The mean activation energy for these rises is 0.66 +/- 0.05 eV, similar to the reported across-species (interspecific) value of 0.65 eV. However, systematic variation in the distribution of rise activation energies is evident, including previously unrecognized right skewness around a median of 0.55 eV. This skewness exists across levels of organization, taxa, trophic groups, and habitats, and it is partially explained by prey having increased trait performance at lower temperatures relative to predators, suggesting a thermal version of the life-dinner principle-stronger selection on running for your life than running for your dinner. For unimodal responses, habitat (marine, freshwater, and terrestrial) largely explains the mean temperature at which trait values are optimal but not variation around the mean. The distribution of activation energies for trait falls has a mean of 1.15 +/- 0.39 eV (significantly higher than rises) and is also right-skewed. Our results highlight generalities and deviations in the thermal response of biological traits and help to provide a basis to predict better how biological systems, from cells to communities, respond to temperature change. +
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On the low dimensionality of behavioral deficits and alterations of brain network connectivity after focal injury +
Traditional neuropsychological approaches emphasize the specificity of behavioral deficits and the modular organization of the brain. At the population level, however, there is emerging evidence that deficits are correlated resulting in a low dimensional structure of post-stroke neurological impairments. Here we consider the implications of low dimensionality for the three-way mapping between structural damage, altered physiology, and behavioral deficits. Understanding this mapping will be aided by large-sample studies that apply multivariate models and focus on explained percentage of variance, as opposed to univariate lesion-symptom techniques that report statistical significance. The low dimensionality of behavioral deficits following stroke is paralleled by widespread, yet relatively consistent, changes in functional connectivity (FC), including a reduction in modularity. Both are related to the structural damage to white matter and subcortical grey commonly produced by stroke. We suggest that large-scale physiological abnormalities following a stroke reduce the variety of neural states visited during task processing and at rest, resulting in a limited repertoire of behavioral states. +
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Translation in cognitive neuroscience remains beyond the horizon, brought no closer by supposed major advances in our understanding of the brain. Unless our explanatory models descend to the individual level—a cardinal requirement for any intervention—their real-world applications will always be limited. Drawing on an analysis of the informational properties of the brain, here we argue that adequate individualisation needs models of far
greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms. We discuss how recent advances in high-performance computing, combined with collections of large-scale
data, enable the high-dimensional modelling we argue is critical to successful translation, and urge its adoption if the ultimate goal of impact on the lives of patients is to be achieved. +
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Understanding how changes in temperature affect interspecific competition is critical for predicting changes in ecological communities as the climate warms. Here we develop a simple theoretical model that links interspecific differences in metabolic traits, which capture the temperature-dependence of resource acquisition, to the outcome of pairwise competition in phytoplankton. We parameterised our model with metabolic traits derived from six species of freshwater phytoplankton and tested its ability to predict the outcome of competition in all pairwise combinations of the species in a factorial experiment, manipulating temperature and nutrient availability. The model correctly predicted the outcome of competition in 71% of the pairwise experiments. These results demonstrate that metabolic traits play a key role in determining how changes in temperature influence interspecific competition and lay the foundation for developing theory to predict the effects of warming in complex, multi-species communities. +
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Understanding the functional impacts of pollinator species losses on plant populations is critical given ongoing pollinator declines. Simulation models of pollination networks suggest that plant communities will be resilient to losing many or even most of the pollinator species in an ecosystem. These predictions, however, have not been tested empirically and implicitly assume that pollination efficacy is unaffected by interactions with interspecific competitors. By contrast, ecological theory and data from a wide range of ecosystems show that interspecific competition can drive variation in ecological specialization over short timescales via behavioral or morphological plasticity, although the potential implications of such changes in specialization for ecosystem functioning remain unexplored. We conducted manipulative field experiments in which we temporarily removed single pollinator species from study plots in subalpine meadows, to test the hypothesis that interactions between pollinator species can shape individual species’ functional roles via changes in foraging specialization. We show that loss of a single pollinator species reduces floral fidelity (short-term specialization) in the remaining pollinators, with significant implications for ecosystem functioning in terms of reduced plant reproduction, even when potentially effective pollinators remained in the system. Our results suggest that ongoing pollinator declines may have more serious negative implications for plant communities than is currently assumed. More broadly, we show that the individual functional contributions of species can be dynamic and shaped by the community of interspecific competitors, thereby documenting a distinct mechanism for how biodiversity can drive ecosystem functioning, with potential relevance to a wide range of taxa and systems. +
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We present a population model to examine the forces that determined the quality and quantity of human life in early agricultural societies where cultivable area is limited. The model is driven by the non-linear and interdependent relationships between the age distribution of a population, its behavior and technology, and the nature of its environment. The common currency in the model is the production of food, on which age-specific rates of birth and death depend. There is a single non-trivial equilibrium population at which productivity balances caloric needs. One of the most powerful controls on equilibrium hunger level is fertility control. Gains against hunger are accompanied by decreases in population size. Increasing worker productivity does increase equilibrium population size but does not improve welfare at equilibrium. As a case study we apply the model to the population of a Polynesian valley before European contact. +
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We typically observe large-scale outcomes that arise from the interactions of many hidden, small-scale processes. Examples include age of disease onset, rates of amino acid substitutions, and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show how the classic patterns such as Poisson and Gaussian arise. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present an informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non-neutral generative processes that attract to the same neutral pattern. +
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Elevated success of multispecies bacterial invasions impacts community composition during ecological succession +
Wrack supply is a common feature of beaches around the world. The species composition and spatial distribution of wrack deposits have been widely recognized as two significant factors in the ecological role of macroalgae within beach ecosystems. These accumulation processes may be intensified by bloom events of opportunistic algae, which frequently occur in estuarine environments. For this study, wrack supply was assessed over 13 months in two estuarine beaches of the Galician coastline, NW Spain. Algal biomass and wrack species distribution were characterized, paying special attention to possible relationships between beach slope, tidal height and algal morphology. The results show that the average values in total monthly biomass of stranded macroalgae and seagrasses were similar for both beaches, but certain differences in temporal and spatial variability throughout the year's cycle were detected. Despite this, the distribution of algal species along the beach face was similar in both beaches: large amounts of sheet-like and thin branched species (e.g. Ulva spp., Gracilaria gracilis) dominated the wrack deposits on the low-tide terraces, whereas thick leathery species with positive flotation properties (e.g. Fucus spp., Ascophyllum nodosum) showed larger amounts in the upper foreshore zones. The relative contribution of the major morphological groups of algal wrack shifted throughout the year, suggesting an inverse relation between thick leathery group and sheet-like species, which could be associated with factors such as differences in macroalgae's life cycles and nutrient requirements of fast- and slow-growing species. This study demonstrates that beach topography, together with the morphology of macroalgae species influence the distribution of wrack deposits in estuarine beaches. Further research on the implications of wrack dynamics for sedimentary characteristics and faunal distribution is needed. © 2012 Elsevier B.V. +
abstract: Eco-evolutionary theory argues that population cycles in consumer-resource interactions are partly driven by natural selection, such that changes in densities and changes in trait values are mutually reinforcing. Evidence that the theory explains cycles in nature, how-ever, is almost nonexistent. Experimental tests of model assumptions are logistically impractical for most organisms, while for others, evi-dence that population cycles occur in nature is lacking. For insect bac-uloviruses in contrast, tests of model assumptions are straightforward, and there is strong evidence that baculoviruses help drive population cycles in many insects, including the gypsy moth that we study here. We therefore used field experiments with the gypsy moth baculovi-rus to test two key assumptions of eco-evolutionary models of host-pathogen population cycles: that reduced host infection risk is heritable and that it is costly. Our experiments confirm both assumptions, and inserting parameters estimated from our data into eco-evolutionary insect-outbreak models gives cycles closely resembling gypsy moth out-break cycles in North America, whereas standard models predict unre-alistic stable equilibria. Our work shows that eco-evolutionary models are useful for explaining outbreaks of forest insect defoliators, while widespread observations of intense selection on defoliators in nature and of heritable and costly resistance in defoliators in the lab together suggest that eco-evolutionary dynamics may play a general role in de-foliator outbreaks. +
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umans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many real networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features. +
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© 2017 The Author(s). Disturbed activity patterns in cortical networks contribute to the pathophysiology of schizophrenia (SZ). Several lines of evidence implicate NMDA receptor hypofunction in SZ, and blocking NMDA receptor signaling during early neurodevelopment produces cognitive deficits in rodent models that resemble those seen in schizophrenic patients. However, the altered network dynamics underlying these cognitive impairments largely remain to be characterized, especially at the cellular level. Here, we use in vivo two-photon calcium imaging to describe pathological dynamics, occurring in parallel with cognitive dysfunction, in a developmental NMDA receptor hypofunction model. We observed increased synchrony and specific alterations in spatiotemporal activity propagation, which could be causally linked to a previously unidentified persistent bursting phenotype. This phenotype was rescued by acute treatment with the NMDA receptor co-agonist D-serine or the GABAB receptor agonist baclofen, which similarly rescued working memory performance. It was not reproduced by optogenetic inhibition of fast-spiking interneurons. These results provide novel insight into network-level abnormalities mediating the cognitive impairment induced by NMDA receptor hypofunction. +
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© 2018 The Author(s). Species invasions constitute a major and poorly understood threat to plant-pollinator systems. General theory predicting which factors drive species invasion success and subsequent effects on native ecosystems is particularly lacking. We address this problem using a consumer-resource model of adaptive behavior and population dynamics to evaluate the invasion success of alien pollinators into plant-pollinator networks and their impact on native species. We introduce pollinator species with different foraging traits into network models with different levels of species richness, connectance, and nestedness. Among 31 factors tested, including network and alien properties, we find that aliens with high foraging efficiency are the most successful invaders. Networks exhibiting high alien-native diet overlap, fraction of alien-visited plant species, most-generalist plant connectivity, and number of specialist pollinator species are the most impacted by invaders. Our results mimic several disparate observations conducted in the field and potentially elucidate the mechanisms responsible for their variability. +
Socioeconomic status moderates age-related differences in the brain’s functional network organization and anatomy across the adult lifespan2 +
© 2018, The Institute of Navigation, Inc. All rights reserved. An individual’s environmental surroundings interact with the development and maturation of their brain. An important aspect of an individual’s environment is his or her socioeconomic status (SES), which estimates access to material resources and social prestige. Previous characterizations of the relation between SES and the brain have primarily focused on earlier or later epochs of the lifespan (i.e., childhood, older age). We broaden this work to examine the relationship between SES and the brain across a wide range of human adulthood (20–89 years), including individuals from the less studied middle-age range. SES, defined by education attainment and occupational socioeconomic characteristics, moderates previously reported age-related differences in the brain’s functional network organization and whole-brain cortical structure. Across middle age (35–64 years), lower SES is associated with reduced resting-state system segregation (a measure of effective functional network organization). A similar but less robust relationship exists between SES and age with respect to brain anatomy: Lower SES is associated with reduced cortical gray matter thickness in middle age. Conversely, younger and older adulthood do not exhibit consistent SES-related difference in the brain measures. The SES–brain relationships persist after controlling for measures of physical and mental health, cognitive ability, and participant demographics. Critically, an individual’s childhood SES cannot account for the relationship between their current SES and functional network organization. These findings provide evidence that SES relates to the brain’s functional network organization and anatomy across adult middle age, and that higher SES may be a protective factor against age-related brain decline. +
