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[b]1.[/b] Plant functional traits, in particular specific leaf area (SLA), wood density and seed mass, are often good predictors of individual tree growth rates within communities. Individuals and species with high SLA, low wood density and small seeds tend to have faster growth rates.[br/][br/][b]2.[/b] If community-level relationships between traits and growth have general predictive value, then similar relationships should also be observed in analyses that integrate across taxa, biogeographic regions and environments. Such global consistency would imply that traits could serve as valuable proxies for the complex suite of factors that determine growth rate, and, therefore, could underpin a new generation of robust dynamic vegetation models. Alternatively, growth rates may depend more strongly on the local environment or growth–trait relationships may vary along environmental gradients.[br/][br/][b]3.[/b] We tested these alternative hypotheses using data on 27 352 juvenile trees, representing 278 species from 27 sites on all forested continents, and extensive functional trait data, 38% of which were obtained at the same sites at which growth was assessed. Data on potential evapotranspiration (PET), which summarizes the joint ecological effects of temperature and precipitation, were obtained from a global data base.[br/][br/][b]4.[/b] We estimated size-standardized relative height growth rates (SGR) for all species, then related them to functional traits and PET using mixed-effect models for the fastest growing species and for all species together.[br/][br/][b]5.[/b] Both the mean and 95th percentile SGR were more strongly associated with functional traits than with PET. PET was unrelated to SGR at the global scale. SGR increased with increasing SLA and decreased with increasing wood density and seed mass, but these traits explained only 3.1% of the variation in SGR. SGR–trait relationships were consistently weak across families and biogeographic zones, and over a range of tree statures. Thus, the most widely studied functional traits in plant ecology were poor predictors of tree growth over large scales.[br/][br/][b]6.[/b] Synthesis. We conclude that these functional traits alone may be unsuitable for predicting growth of trees over broad scales. Determining the functional traits that predict vital rates under specific environmental conditions may generate more insight than a monolithic global relationship can offer.

Cyclic fluctuations in abundance exhibited by some mammalian populations in northern habitats (“population cycles”) are key processes in the functioning of many boreal and tundra ecosystems. Understanding population cycles, essentially demographic processes, necessitates discerning the demographic mechanisms that underlie numerical changes. Using mark–recapture data spanning five population cycles (1977–2017), we examined demographic mechanisms underlying the 9–10-yr cycles exhibited by snowshoe hares (Lepus americanus Erxleben) in southwestern Yukon, Canada. Snowshoe hare populations always decreased during winter and increased during summer; the balance between winter declines and summer increases characterized the four, multiyear cyclic phases: increase, peak, decline, and low. Little or no recruitment occurred during winter, but summer recruitment varied markedly across the four phases with the highest and lowest recruitment observed during the increase and decline phase, respectively. Population crashes during the decline were triggered by a substantial decline in winter survival and by a lack of subsequent summer recruitment. In contrast, initiation of the increase phase was triggered by a twofold increase in summer recruitment abetted secondarily by improvements in subsequent winter survival. We show that differences in peak density across cycles are explained by differences in overall population growth rate, amount of time available for population growth to occur, and starting population density. Demographic mechanisms underlying snowshoe hare population cycles were consistent across cycles in our study site but we do not yet know if similar demographic processes underlie population cycles in other northern snowshoe hare populations.

Secular stagnation on the supply side takes the form of a slow 1.6 percent annual growth rate of US potential real GDP, roughly half the 3.1 percent annual growth rate of actual real GDP realized from 1972 to 2004. This slowdown stems from a sharp decline in the growth rate of aggregate hours of work and of output per hour. This paper attributes the productivity growth decline to diminishing returns in the digital revolution that had its peak effect business hardware, software, and best practices in the late 1990s but has resulted in little change in those methods over the past decade.

Thomas Piketty's (2013) book, Capital in the 21st Century, follows in the tradition of the great classical economists, like Marx and Ricardo, in formulating general laws of capitalism to diagnose and predict the dynamics of inequality. We argue that general economic laws are unhelpful as a guide to understanding the past or predicting the future because they ignore the central role of political and economic institutions, as well as the endogenous evolution of technology, in shaping the distribution of resources in society. We use regression evidence to show that the main economic force emphasized in Piketty's book, the gap between the interest rate and the growth rate, does not appear to explain historical patterns of inequality (especially, the share of income accruing to the upper tail of the distribution). We then use the histories of inequality of South Africa and Sweden to illustrate that inequality dynamics cannot be understood without embedding economic factors in the context of economic and political institutions, and also that the focus on the share of top incomes can give a misleading characterization of the true nature of inequality.

When a lengthy book is widely discussed in academic circles and the popular media, it is probably inevitable that the arguments of the book will be simplified in the telling and retelling. In the case of my book Capital in the Twenty-First Century (2014), a common simplification of the main theme is that because the rate of return on capital r exceeds the growth rate of the economy g, the inequality of wealth is destined to increase indefinitely over time. In my view, the magnitude of the gap between r and g is indeed one of the important forces that can explain historical magnitudes and variations in wealth inequality. However, I do not view r > g as the only or even the primary tool for considering changes in income and wealth in the 20th century, or for forecasting the path of income and wealth inequality in the 21st century. In this essay, I will take up several themes from my book that have perhaps become attenuated or garbled in the ongoing discussions of the book, and will seek to re-explain and re-frame these themes. First, I stress the key role played in my book by the interaction between beliefs systems, institutions, and the dynamics of inequality. Second, I briefly describe my multidimensional approach to the history of capital and inequality. Third, I review the relationship and differing causes between wealth inequality and income inequality. Fourth, I turn to the specific role of r > g in the dynamics of wealth inequality: specifically, a larger r − g gap will amplify the steady-state inequality of a wealth distribution that arises out of a given mixture of shocks. Fifth, I consider some of the scenarios that affect how r − g might evolve in the 21st century, including rising international tax competition, a growth slowdown, and differential access by the wealthy to higher returns on capital. Finally, I seek to clarify what is distinctive in my historical and political economy approach to institutions and inequality dynamics, and the complementarity with other approaches.

ABSTRACT One of the major parameters that characterizes the kinetics of microbial processes is the maximum specific growth rate. The maximum specific growth rate for a single microorganism (${\\mu _{max}}$) is fairly constant. However, a certain microbial process is typically catalyzed by a group of microorganisms (guild) that have various ${\\mu _{max}}$ values. In many occasions, it is not feasible to breakdown a guild into its constituent microorganisms. Therefore, it is a common practice to assume a constant maximum specific growth rate for the guild ($\\acute{\\mu}_{max}$) and determine its value by fitting experimental data. This assumption is valid for natural environments, where microbial guilds are stabilized and dominated by microorganisms that grow optimally in those environments’ conditions. However, a change in an environment's conditions will trigger a community shift by favoring some of the microorganisms. This shift leads to a variable ${\\acute{\\mu}_{max}}$ as long as substrate availability is significantly higher than substrate affinity constant. In this work, it is illustrated that the assumption of constant ${\\acute{\\mu}_{max}}$ may underestimate or overestimate microbial growth. To circumvent this, a novel relationship that characterizes changes in ${\\acute{\\mu}_{max}}$ under abundant nutrient availability is proposed. The proposed relationship is evaluated for various random microbial guilds in batch experiments. The maximum specific growth rate of a microbial guild is not a constant but a variable that can be estimated with the proposed relationship.

We propose a latent variables approach within a present-value model to estimate the expected returns and expected dividend growth rates of the aggregate stock market. This approach aggregates information contained in the history of price-dividend ratios and dividend growth rates to predict future returns and dividend growth rates. We find that returns and dividend growth rates are predictable with R² values ranging from 8.2% to 8.9% for returns and 13.9% to 31.6% for dividend growth rates. Both expected returns and expected dividend growth rates have a persistent component, but expected returns are more persistent than expected dividend growth rates.

I estimate the impact of hurricane strikes on local economic growth rates. To this end, I assemble a panel data set of U.S. coastal counties' growth rates and construct a novel hurricane destruction index that is based on a monetary loss equation, local wind speed estimates derived from a physical wind field model, and local exposure characteristics. The econometric results suggest that a county's annual economic growth rate falls on average by 0.45 percentage points, 28% of it due to richer individuals moving away from affected counties. I also find that the impact of hurricanes is netted out in annual terms at the state level and does not affect national economic growth rates at all.

Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering. Synopsis The GECKO method takes into account enzyme abundances and kinetics to enhance genome‐scale models of metabolism (GEMs). An implementation for Saccharomyces cerevisiae gives insight into metabolism and enzyme usage. GECKO is a method that enhances a GEM with enzyme constraints, using both kinetic and omics data. The enzyme‐constrained ecYeast7 model of S. cerevisiae outperforms previous models in simulation capabilities and allows exploring enzyme usage. Directly integrating quantitative proteomic data in ecYeast7 significantly reduces the inherent flux variability of model simulations. Physiological behavior such as maximum specific growth rate, overflow metabolism and gene deletion response can be explained by a limited enzyme pool in cell. Graphical Abstract The GECKO method takes into account enzyme abundances and kinetics to enhance genome‐scale models of metabolism (GEMs). An implementation for Saccharomyces cerevisiae gives insight into metabolism and enzyme usage.

Ecologists seek general explanations for the dramatic variation in species abundances in space and time. An increasingly popular solution is to predict species distributions, dynamics, and responses to environmental change based on easily measured anatomical and morphological traits. Trait-based approaches assume that simple functional traits influence fitness and life history evolution, but rigorous tests of this assumption are lacking, because they require quantitative information about the full lifecycles of many species representing different life histories. Here, we link a global traits database with empirical matrix population models for 222 species and report strong relationships between functional traits and plant life histories. Species with large seeds, long-lived leaves, or dense wood have slow life histories, with mean fitness (i.e., population growth rates) more strongly influenced by survival than by growth or fecundity, compared with fast life history species with small seeds, short-lived leaves, or soft wood. In contrast to measures of demographic contributions to fitness based on whole lifecycles, analyses focused on raw demographic rates may underestimate the strength of association between traits and mean fitness. Our results help establish the physiological basis for plant life history evolution and show the potential for trait-based approaches in population dynamics.