Showing papers by "Santa Fe Institute published in 2017"

The ground truth about metadata and community detection in networks

Abstract: Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.

Snowball Earth climate dynamics and Cryogenian geology-geobiology

Abstract: Geological evidence indicates that grounded ice sheets reached sea level at all latitudes during two long-lived Cryogenian (58 and ≥5 My) glaciations. Combined uranium-lead and rhenium-osmium dating suggests that the older (Sturtian) glacial onset and both terminations were globally synchronous. Geochemical data imply that CO2 was 102 PAL (present atmospheric level) at the younger termination, consistent with a global ice cover. Sturtian glaciation followed breakup of a tropical supercontinent, and its onset coincided with the equatorial emplacement of a large igneous province. Modeling shows that the small thermal inertia of a globally frozen surface reverses the annual mean tropical atmospheric circulation, producing an equatorial desert and net snow and frost accumulation elsewhere. Oceanic ice thickens, forming a sea glacier that flows gravitationally toward the equator, sustained by the hydrologic cycle and by basal freezing and melting. Tropical ice sheets flow faster as CO2 rises but lose mass and become sensitive to orbital changes. Equatorial dust accumulation engenders supraglacial oligotrophic meltwater ecosystems, favorable for cyanobacteria and certain eukaryotes. Meltwater flushing through cracks enables organic burial and submarine deposition of airborne volcanic ash. The subglacial ocean is turbulent and well mixed, in response to geothermal heating and heat loss through the ice cover, increasing with latitude. Terminal carbonate deposits, unique to Cryogenian glaciations, are products of intense weathering and ocean stratification. Whole-ocean warming and collapsing peripheral bulges allow marine coastal flooding to continue long after ice-sheet disappearance. The evolutionary legacy of Snowball Earth is perceptible in fossils and living organisms.

The multilayer nature of ecological networks.

Abstract: Although networks provide a powerful approach to study a large variety of ecological systems, their formulation does not typically account for multiple interaction types, interactions that vary in space and time, and interconnected systems such as networks of networks. The emergent field of ‘multilayer networks’ provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it specifically allows one to differentiate and model ‘intralayer’ and ‘interlayer’ connectivity. The framework provides a set of concepts and tools that can be adapted and applied to ecology, facilitating research on high-dimensional, heterogeneous systems in nature. Here, we formally define ecological multilayer networks based on a review of previous, related approaches; illustrate their application and potential with analyses of existing data; and discuss limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers largely untapped potential to investigate ecological complexity and provide new theoretical and empirical insights into the architecture and dynamics of ecological systems. Ecological interactions typically vary across both space and time. Here, the authors outline a framework for incorporating multiple layers of complexity into ecological networks, and discuss their potential applications and future challenges.

Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models.

Abstract: Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18-34°C with maximal transmission occurring in a range from 26-29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.

The Economic Consequences of Social-Network Structure

Abstract: We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of "macro" and "micro" characteristics of social-interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors.

Ecological plant epigenetics: Evidence from model and non-model species, and the way forward

Abstract: Growing evidence shows that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology. In plant ecology, recent studies have attempted to merge ecological experiments with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress responses, adaptation to habitat, and range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which contribute to a more mechanistic understanding but have limited ecological realism. Understanding the significance of epigenetics for plant ecology requires increased transfer of knowledge and methods from model species research to genomes of evolutionarily divergent species, and examination of responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration among molecular geneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database

Abstract: There is an urgent need for large-scale botanical data to improve our understanding of community assembly, coexistence, biogeography, evolution, and many other fundamental biological processes. Understanding these processes is critical for predicting and handling human-biodiversity interactions and global change dynamics such as food and energy security, ecosystem services, climate change, and species invasions. The Botanical Information and Ecology Network (BIEN) database comprises an unprecedented wealth of cleaned and standardised botanical data, containing roughly 81 million occurrence records from c. 375,000 species, c. 915,000 trait observations across 28 traits from c. 93,000 species, and co-occurrence records from 110,000 ecological plots globally, as well as 100,000 range maps and 100 replicated phylogenies (each containing 81,274 species) for New World species. Here, we describe an r package that provides easy access to these data. The bien r package allows users to access the multiple types of data in the BIEN database. Functions in this package query the BIEN database by turning user inputs into optimised PostgreSQL functions. Function names follow a convention designed to make it easy to understand what each function does. We have also developed a protocol for providing customised citations and herbarium acknowledgements for data downloaded through the bien r package. The development of the BIEN database represents a significant achievement in biological data integration, cleaning and standardization. Likewise, the bien r package represents an important tool for open science that makes the BIEN database freely and easily accessible to everyone.

Community detection, link prediction, and layer interdependence in multilayer networks.

Abstract: Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

New approaches for delineating n-dimensional hypervolumes

Abstract: 1.Hutchinson's n-dimensional hypervolume concept underlies many applications in contemporary ecology and evolutionary biology. Estimating hypervolumes from sampled data has been an ongoing challenge due to conceptual and computational issues. 2.We present new algorithms for delineating the boundaries and probability density within n-dimensional hypervolumes. The methods produce smooth boundaries that can fit data either more loosely (Gaussian kernel density estimation) or more tightly (one-classification via support vector machine). Further, the algorithms can accept abundance-weighted data, and the resulting hypervolumes can be given a probabilistic interpretation and projected into geographic space. 3.We demonstrate the properties of these methods on a large dataset that characterizes the functional traits and geographic distribution of thousands of plants. The methods are available in version ≥2.0.6 of the hypervolume R package. 4.These new algorithms provide: (i) a more robust approach for delineating the shape and density of n-dimensional hypervolumes; (ii) more efficient performance on large and high-dimensional datasets; and (iii) improved measures of functional diversity and environmental niche breadth. This article is protected by copyright. All rights reserved.

Trait variation and integration across scales: is the leaf economic spectrum present at local scales?

Abstract: Trait-based approaches have taken an increasingly dominant role in community ecology. Although trait-based strategy dimensions such as the leaf economic spectrum (LES) have been identified primarily at global-scales, trait variation at the community scale is often interpreted in this context. Here we argue from several lines of evidence that a research priority should be to determine whether global-scale trait relationships hold at more local scales. We review recent literature assessing trait variation at smaller scales, and then present a case study exploring the relationship between the correlation strength of leaf traits and their similarity in variation structure across ecological scales. We find that the correlation strength between pairs of leaf traits does not predict whether the traits respond similarly to different drivers of variation. Instead, correlation strength only sets an upper bound to the dissimilarity in trait variation structure. With moderate correlation strengths, LES traits largely retain the ability to respond independently to different drivers of phenotypic variation at different scales. Recent literature and our results suggest that LES relationships may not hold at local scales. Clarifying under what conditions and at which scales the LES is consistently expressed is necessary for us to make the most of the emerging trait toolbox.

Greater post-Neolithic wealth disparities in Eurasia than in North America and Mesoamerica

Abstract: Analyses of house-size distributions in the Old and New World showed that wealth disparities increased with the domestication of plants and animals and with increased sociopolitical scale. Beneath headlines about booms and busts and other economic disturbance lies a deeper problem: wealth inequality. But what is its history, and what are the larger social factors that determine the disparate distribution of wealth? Timothy Kohler and colleagues look at the evolution of inequality worldwide since the Neolithic era, around 11,000 years ago, using house size as a proxy for calculating the Gini coefficient, a measure of wealth inequality. The study shows that, as may be expected, wealth inequality has generally increased. Unexpectedly however, inequality increased far more in the Old World of Europe and Asia than in the New (North and Central America). Even in highly urban New World sites, house sizes are generally similar. There are no enormous palaces, which one expects in Old World urban contexts. The authors suggest that the inherent wealth provided by large domesticated animals could explain the imbalance. Horses, for example, allowed people to ride around acquiring wealth from others. How wealth is distributed among households provides insight into the fundamental characters of societies and the opportunities they afford for social mobility1,2. However, economic inequality has been hard to study in ancient societies for which we do not have written records3,4, which adds to the challenge of placing current wealth disparities into a long-term perspective. Although various archaeological proxies for wealth, such as burial goods5,6 or exotic or expensive-to-manufacture goods in household assemblages7, have been proposed, the first is not clearly connected with households, and the second is confounded by abandonment mode and other factors. As a result, numerous questions remain concerning the growth of wealth disparities, including their connection to the development of domesticated plants and animals and to increases in sociopolitical scale8. Here we show that wealth disparities generally increased with the domestication of plants and animals and with increased sociopolitical scale, using Gini coefficients computed over the single consistent proxy of house-size distributions. However, unexpected differences in the responses of societies to these factors in North America and Mesoamerica, and in Eurasia, became evident after the end of the Neolithic period. We argue that the generally higher wealth disparities identified in post-Neolithic Eurasia were initially due to the greater availability of large mammals that could be domesticated, because they allowed more profitable agricultural extensification9, and also eventually led to the development of a mounted warrior elite able to expand polities (political units that cohere via identity, ability to mobilize resources, or governance) to sizes that were not possible in North America and Mesoamerica before the arrival of Europeans10,11. We anticipate that this analysis will stimulate other work to enlarge this sample to include societies in South America, Africa, South Asia and Oceania that were under-sampled or not included in this study.

Data-driven predictions in the science of science.

Abstract: The desire to predict discoveries—to have some idea, in advance, of what will be discovered, by whom, when, and where—pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the “science of science” and what it teaches us about the predictability of scientific discovery. We then discuss future opportunities for improving predictions derived from the science of science and its potential impact, positive and negative, on the scientific community.

Heterogeneity and scale of sustainable development in cities

Abstract: Rapid worldwide urbanization is at once the main cause and, potentially, the main solution to global sustainable development challenges. The growth of cities is typically associated with increases in socioeconomic productivity, but it also creates strong inequalities. Despite a growing body of evidence characterizing these heterogeneities in developed urban areas, not much is known systematically about their most extreme forms in developing cities and their consequences for sustainability. Here, we characterize the general patterns of income and access to services in a large number of developing cities, with an emphasis on an extensive, high-resolution analysis of the urban areas of Brazil and South Africa. We use detailed census data to construct sustainable development indices in hundreds of thousands of neighborhoods and show that their statistics are scale-dependent and point to the critical role of large cities in creating higher average incomes and greater access to services within their national context. We then quantify the general statistical trajectory toward universal basic service provision at different scales to show that it is characterized by varying levels of inequality, with initial increases in access being typically accompanied by growing disparities over characteristic spatial scales. These results demonstrate how extensions of these methods to other goals and data can be used over time and space to produce a simple but general quantitative assessment of progress toward internationally agreed sustainable development goals.

A broadly distributed toxin family mediates contact-dependent antagonism between gram-positive bacteria

Abstract: The Firmicutes are a phylum of bacteria that dominate numerous polymicrobial habitats of importance to human health and industry. Although these communities are often densely colonized, a broadly distributed contact-dependent mechanism of interbacterial antagonism utilized by Firmicutes has not been elucidated. Here we show that proteins belonging to the LXG polymorphic toxin family present in Streptococcus intermedius mediate cell contact- and Esx secretion pathway-dependent growth inhibition of diverse Firmicute species. The structure of one such toxin revealed a previously unobserved protein fold that we demonstrate directs the degradation of a uniquely bacterial molecule required for cell wall biosynthesis, lipid II. Consistent with our functional data linking LXG toxins to interbacterial interactions in S. intermedius, we show that LXG genes are prevalent in the human gut microbiome, a polymicrobial community dominated by Firmicutes. We speculate that interbacterial antagonism mediated by LXG toxins plays a critical role in shaping Firmicute-rich bacterial communities.

Interspecific integration of trait dimensions at local scales: the plant phenotype as an integrated network

Abstract: Summary Plant phenotypic diversity is shaped by the interplay of trade-offs and constraints in evolution. Closely integrated groups of traits (i.e. trait dimensions) are used to classify plant phenotypic diversity into plant strategies, but we do not know the degree of interdependence among trait dimensions. To assess how selection has shaped the phenotypic space, we examine whether trait dimensions are independent. We gathered data on saplings of 24 locally coexisting tree species in a temperate forest, and examined the correlation structure of 20 leaf, branch, stem and root traits. These traits fall into three well-established trait dimensions (the leaf economic spectrum, the wood spectrum and Corner's Rules) that characterize vital plant functions: resource acquisition, sap transport, mechanical support and canopy architecture. Using ordinations, network analyses and Mantel tests, we tested whether the sapling phenotype of these tree species is organized along independent trait dimensions. Across species, the sapling phenotype is not structured into clear trait dimensions. The trait relationships defining trait dimensions are either weak or absent and do not dominate the correlation structure of the sapling phenotype as a whole. Instead traits from the three commonly recognized trait dimensions are organized into an integrated trait network. The effect of phylogeny on trait correlations is minimal. Our results indicate that trait dimensions apparent in broad-based interspecific surveys do not hold up among locally coexisting species. Furthermore, architectural traits appear central to the phenotypic network, suggesting a pivotal role for branching architecture in linking resource acquisition, mechanical support and hydraulic functions. Synthesis. Our study indicates that local and global patterns of phenotypic integration differ and calls into question the use of trait dimensions at local scales. We propose that a network approach to assessing plant function more effectively reflects the multiple trade-offs and constraints shaping the phenotype in locally co-occurring species.

The Tsimane Health and Life History Project: Integrating anthropology and biomedicine.

Abstract: The Tsimane Health and Life History Project, an integrated bio-behavioral study of the human life course, is designed to test competing hypotheses of human life-history evolution. One aim is to understand the bidirectional connections between life history and social behavior in a high-fertility, kin-based context lacking amenities of modern urban life (e.g. sanitation, banks, electricity). Another aim is to understand how a high pathogen burden influences health and well-being during development and adulthood. A third aim addresses how modernization shapes human life histories and sociality. Here we outline the project's goals, history, and main findings since its inception in 2002. We reflect on the implications of current findings and highlight the need for more coordinated ethnographic and biomedical study of contemporary nonindustrial populations to address broad questions that can situate evolutionary anthropology in a key position within the social and life sciences.

Fundamental trade-offs between information flow in single cells and cellular populations.

Abstract: Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3–4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.

Drivers of Bacterial Maintenance and Minimal Energy Requirements.

Abstract: Microbes maintain themselves through a variety of processes. Several of these processes can be reduced or shut down entirely when resource availability declines. In pure culture conditions with ample substrate supply, a relationship between the maximum growth rate and the energy invested in maintenance has been reported widely. However, at the other end of the resources spectrum, bacteria are so extremely limited by energy that no growth occurs and metabolism is constrained to the most essential functions only. These minimum energy requirements have been called the basal power requirement. While seemingly different from each other, both aspects are likely components of a continuum of regulated maintenance processes. Here, we analyze cross species tradeoffs in cellular physiology over the range of bacterial size and energy expenditure and determine the contributions to maintenance metabolism at each point along the size-energy spectrum. Furthermore, by exploring the simplest bacteria within this framework-- which are most affected by maintenance constraints-- we uncover which processes become most limiting. For the smallest species, maintenance metabolism converges on total metabolism, where we predict that maintenance is dominated by the repair of proteins. For larger species the relative costs of protein repair decrease and maintenance metabolism is predicted to be dominated by the repair of RNA components. These results provide new insights into which processes are likely to be regulated in environments that are extremely limited by energy.

Why do people use electronic nicotine delivery systems (electronic cigarettes)? A content analysis of Twitter, 2012-2015.

Abstract: The reasons for using electronic nicotine delivery systems (ENDS) are poorly understood and are primarily documented by expensive cross-sectional surveys that use preconceived close-ended response options rather than allowing respondents to use their own words. We passively identify the reasons for using ENDS longitudinally from a content analysis of public postings on Twitter. All English language public tweets including several ENDS terms (e.g., "e-cigarette" or "vape") were captured from the Twitter data stream during 2012 and 2015. After excluding spam, advertisements, and retweets, posts indicating a rationale for vaping were retained. The specific reasons for vaping were then inferred based on a supervised content analysis using annotators from Amazon's Mechanical Turk. During 2012 quitting combustibles was the most cited reason for using ENDS with 43% (95%CI 39-48) of all reason-related tweets cited quitting combustibles, e.g., "I couldn't quit till I tried ecigs," eclipsing the second most cited reason by more than double. Other frequently cited reasons in 2012 included ENDS's social image (21%; 95%CI 18-25), use indoors (14%; 95%CI 11-17), flavors (14%; 95%CI 11-17), safety relative to combustibles (9%; 95%CI 7-11), cost (3%; 95%CI 2-5) and favorable odor (2%; 95%CI 1-3). By 2015 the reasons for using ENDS cited on Twitter had shifted. Both quitting combustibles and use indoors significantly declined in mentions to 29% (95%CI 24-33) and 12% (95%CI 9-16), respectively. At the same time, social image increased to 37% (95%CI 32-43) and lack of odor increased to 5% (95%CI 2-5), the former leading all cited reasons in 2015. Our data suggest the reasons people vape are shifting away from cessation and toward social image. The data also show how the ENDS market is responsive to a changing policy landscape. For instance, smoking indoors was less frequently cited in 2015 as indoor smoking restrictions became more common. Because the data and analytic approach are scalable, adoption of our strategies in the field can inform follow-up survey-based surveillance (so the right questions are asked), interventions, and policies for ENDS.

Vitamin D receptor regulates autophagy in the normal mammary gland and in luminal breast cancer cells

Abstract: Women in North America have a one in eight lifetime risk of developing breast cancer (BC), and a significant proportion of these individuals will develop recurrent BC and will eventually succumb to the disease. Metastatic, therapy-resistant BC cells are refractory to cell death induced by multiple stresses. Here, we document that the vitamin D receptor (VDR) acts as a master transcriptional regulator of autophagy. Activation of the VDR by vitamin D induces autophagy and an autophagic transcriptional signature in BC cells that correlates with increased survival in patients; strikingly, this signature is present in the normal mammary gland and is progressively lost in patients with metastatic BC. A number of epidemiological studies have shown that sufficient vitamin D serum levels might be protective against BC. We observed that dietary vitamin D supplementation in mice increases basal levels of autophagy in the normal mammary gland, highlighting the potential of vitamin D as a cancer-preventive agent. These findings point to a role of vitamin D and the VDR in modulating autophagy and cell death in both the normal mammary gland and BC cells.

The misleading narrative of the canonical faculty productivity trajectory

Abstract: A scientist may publish tens or hundreds of papers over a career, but these contributions are not evenly spaced in time. Sixty years of studies on career productivity patterns in a variety of fields suggest an intuitive and universal pattern: Productivity tends to rise rapidly to an early peak and then gradually declines. Here, we test the universality of this conventional narrative by analyzing the structures of individual faculty productivity time series, constructed from over 200,000 publications and matched with hiring data for 2,453 tenure-track faculty in all 205 PhD-granting computer science departments in the United States and Canada. Unlike prior studies, which considered only some faculty or some institutions, or lacked common career reference points, here we combine a large bibliographic dataset with comprehensive information on career transitions that covers an entire field of study. We show that the conventional narrative confidently describes only one-fifth of faculty, regardless of department prestige or researcher gender, and the remaining four-fifths of faculty exhibit a rich diversity of productivity patterns. To explain this diversity, we introduce a simple model of productivity trajectories and explore correlations between its parameters and researcher covariates, showing that departmental prestige predicts overall individual productivity and the timing of the transition from first- to last-author publications. These results demonstrate the unpredictability of productivity over time and open the door for new efforts to understand how environmental and individual factors shape scientific productivity.

Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient

Abstract: One of the major challenges in ecology is to understand how ecosystems respond to changes in environmental conditions, and how taxonomic and functional diversity mediate these changes. In this study, we use a trait-spectra and individual-based model, to analyse variation in forest primary productivity along a 3.3 km elevation gradient in the Amazon-Andes. The model accurately predicted the magnitude and trends in forest productivity with elevation, with solar radiation and plant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, and wood density) collectively accounting for productivity variation. Remarkably, explicit representation of temperature variation with elevation was not required to achieve accurate predictions of forest productivity, as trait variation driven by species turnover appears to capture the effect of temperature. Our semi-mechanistic model suggests that spatial variation in traits can potentially be used to estimate spatial variation in productivity at the landscape scale.

Coarse-graining as a downward causation mechanism.

Abstract: Downward causation is the controversial idea that ‘higher’ levels of organization can causally influence behaviour at ‘lower’ levels of organization. Here I propose that we can gain traction on downward causation by being operational and examining how adaptive systems identify regularities in evolutionary or learning time and use these regularities to guide behaviour. I suggest that in many adaptive systems components collectively compute their macroscopic worlds through coarse-graining. I further suggest we move from simple feedback to downward causation when components tune behaviour in response to estimates of collectively computed macroscopic properties. I introduce a weak and strong notion of downward causation and discuss the role the strong form plays in the origins of new organizational levels. I illustrate these points with examples from the study of biological and social systems and deep neural networks. This article is part of the themed issue ‘Reconceptualizing the origins of life’.

Spatial competition constrains resistance to targeted cancer therapy

Abstract: Adaptive therapy (AT) aims to control tumour burden by maintaining therapy-sensitive cells to exploit their competition with resistant cells. This relies on the assumption that resistant cells have impaired cellular fitness. Here, using a model of resistance to a pharmacological cyclin-dependent kinase inhibitor (CDKi), we show that this assumption is valid when competition between cells is spatially structured. We generate CDKi-resistant cancer cells and find that they have reduced proliferative fitness and stably rewired cell cycle control pathways. Low-dose CDKi outperforms high-dose CDKi in controlling tumour burden and resistance in tumour spheroids, but not in monolayer culture. Mathematical modelling indicates that tumour spatial structure amplifies the fitness penalty of resistant cells, and identifies their relative fitness as a critical determinant of the clinical benefit of AT. Our results justify further investigation of AT with kinase inhibitors.

Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome.

Abstract: Although the gut microbiome plays important roles in host physiology, health and disease1, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut. We used the genetically diverse Collaborative Cross mouse system2 to discover that early life history impacts the microbiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes in the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.

Explaining the prevalence, scaling and variance of urban phenomena

Abstract: The prevalence of many urban phenomena changes systematically with population size1. We propose a theory that unifies models of economic complexity2,3 and cultural evolution4 to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city. Gomez-Lievano and colleagues develop a new theory of scaling in cities — how the prevalence of phenomena such as education and crime changes with population size — by unifying models of economic complexity and cultural evolution.

A thousand empirical adaptive landscapes and their navigability

Abstract: The adaptive landscape is an iconic metaphor that pervades evolutionary biology. It was mostly applied in theoretical models until recent years, when empirical data began to allow partial landscape reconstructions. Here, we exhaustively analyse 1,137 complete landscapes from 129 eukaryotic species, each describing the binding affinity of a transcription factor to all possible short DNA sequences. We find that the navigability of these landscapes through single mutations is intermediate to that of additive and shuffled null models, suggesting that binding affinity-and thereby gene expression-is readily fine-tuned via mutations in transcription factor binding sites. The landscapes have few peaks that vary in their accessibility and in the number of sequences they contain. Binding sites in the mouse genome are enriched in sequences found in the peaks of especially navigable landscapes and the genetic diversity of binding sites in yeast increases with the number of sequences in a peak. Our findings suggest that landscape navigability may have contributed to the enormous success of transcriptional regulation as a source of evolutionary adaptations and innovations.