Metabolic Scaling: A New Perspective Based on Scaling of Glycolytic Enzyme Activities

The decline of mass specific aerobic metabolic rates with increasinganimal size has a long history of study in zoology. Attemptsto explain this phenomenon have generally been concerned onlywith aerobic metabolism and with estimators of muscle and skeletalstrength. Our finding of tremendous increases in mass-specificglycolytic enzyme activity in locomotory muscle with size insome species of pelagic fishes indicates that this approachhas been too narrow. It is necessary to consider total metabolicpower in any consideration of metabolic scaling in relationto skeletal strength or muscle power, since the anaerobic componentof muscle power is usually greater than the aerobic and oftenscales differently. We show that scaling of glycolytic powerappears to be much more variable among species than is scalingof aerobic power, and we suggest that the different glycolyticpower scaling patterns reflect selection for different sprintswimming abilities in fishes of different habits. The rathernarrow range of variation in aerobic scaling patterns suggeststhat they are the result of natural selection acting in thecontext of geometric constraints on maximum aerobic gas uptakeand transport. The glycolytic scaling data emphasize that therole of natural selection has usually been neglected in considerationsof scaling of metabolism while the role of the scaling of solidshas been overemphasized.     

Statistical analyses support power law distributions found in neuronal avalanches

The size distribution of neuronal avalanches in cortical networks has been reported to follow a power law distribution with exponent close to -1.5, which is a reflection of long-range spatial correlations in spontaneous neuronal activity. However, identifying power law scaling in empirical data can be difficult and sometimes controversial. In the present study, we tested the power law hypothesis for neuronal avalanches by using more stringent statistical analyses. In particular, we performed the following steps: (i) analysis of finite-size scaling to identify scale-free dynamics in neuronal avalanches, (ii) model parameter estimation to determine the specific exponent of the power law, and (iii) comparison of the power law to alternative model distributions. Consistent with critical state dynamics, avalanche size distributions exhibited robust scaling behavior in which the maximum avalanche size was limited only by the spatial extent of sampling ("finite size" effect). This scale-free dynamics suggests the power law as a model for the distribution of avalanche sizes. Using both the Kolmogorov-Smirnov statistic and a maximum likelihood approach, we found the slope to be close to -1.5, which is in line with previous reports. Finally, the power law model for neuronal avalanches was compared to the exponential and to various heavy-tail distributions based on the Kolmogorov-Smirnov distance and by using a log-likelihood ratio test. Both the power law distribution without and with exponential cut-off provided significantly better fits to the cluster size distributions in neuronal avalanches than the exponential, the lognormal and the gamma distribution. In summary, our findings strongly support the power law scaling in neuronal avalanches, providing further evidence for critical state dynamics in superficial layers of cortex.     

The observed range for temporal mean-variance scaling exponents can be explained by reproductive correlation

The mean-variance scaling relationship known as Taylor's power law has been well documented empirically over the past four decades but a general theoretical explanation for the phenomenon does not exist. Here we provide an explanation that relates empirical patterns of temporal mean-variance scaling to individual level reproductive behavior. Initially, we review the scaling behavior of population growth models to establish theoretical limits for the scaling exponent b that is in agreement with the empirically observed range (1≤b≤2). We go on to show that the degree of reproductive covariance among individuals determines the scaling exponent b. Independent reproduction results in an exponent of one, while completely correlated reproduction results in the upper limit of two. Intermediate exponents, which are common empirically, can be generated through the decay of reproductive covariance with increasing population size. Finally, we describe how the link between reproductive correlation and the scaling exponent provides a way to infer properties of individual-level reproductive behavior, such as the relative influence of demographic stochasticity, from a macroecological pattern.     

Universities Scale Like Cities

Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this paper we demonstrate that universities show a similar behavior as cities in the distribution of the ‘gross university income’ in terms of total number of citations over ‘size’ in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling. We find that deviations from the expected behavior can indeed be explained by specific local properties of universities, particularly the field-specific composition of a university, and its quality in terms of field-normalized citation impact. By studying both the set of the 500 largest universities worldwide and a specific subset of these 500 universities -the top-100 European universities- we are also able to distinguish between properties of universities with as well as without selection of one specific local property, the quality of a university in terms of its average field-normalized citation impact. It also reveals an interesting observation concerning the working of a crucial property in networked systems, preferential attachment.     

Ecological scaling laws link individual body size variation to population abundance fluctuation

Scaling research has seen remarkable progress in the past several decades. Many scaling relationships were discovered within and across individual and population levels, such as species–abundance relationship, Taylor's law, and density mass allometry. However none of these established patterns incorporate individual variation in the formulation. Individual body size variation is a key evolutionary phenomenon and closely related to ecological diversity and species adaptation. Using a macroecological approach, I test 57 Long‐Term Ecological Research data sets and show that a power‐law and a generalized power‐law function describe well the mean‐variance scaling of individual body mass. This relationship connects Taylor's law and density mass allometry, and leads to a new scaling pattern between the individual body size variation and population abundance fluctuation, which is confirmed using freshwater fish and forest tree data. Underlying mechanisms and implications of the proposed scaling relationships are discussed. This synthesis shows that integration and extension of existing ecological laws can lead to the discovery of new scaling patterns and complete our understanding of the relation between individual trait and population abundance. Synthesis Scaling relationships are useful for community ecology as they reveal ubiquitous patterns across different levels of biological organizations. This work extends and integrates two existing scaling laws: Taylor's law and density‐mass allometry, and derives a new variance allometry between individual body mass and population abundance. The result shows that diverse individual body size is associated with stable population fluctuation, reflecting the effect of individual traits on population characteristics. Confirmed by several empirical data sets, these scaling relationships suggest new ways to study the underlying mechanisms of Taylor's law and have profound implications for fisheries and other applied sciences.     

北京东灵山土壤动物多样性海拔格局的尺度推绎规律

识别群落内部各类群多样性格局的复杂性是生态学家面临的挑战,而尺度推绎规律是揭示复杂生态结构的有效途径之一。本研究利用多重分形的方法探索了不同海拔土壤动物多样性格局的尺度推绎规律,对比分析了凋落物层和土壤层之间多重分形谱的差异。结果表明: 与之前对植物群落的分析结果相似,土壤动物多样性尺度推绎规律同样具有幂律特征,如丰富度、Shannon多样性指数和Simpson多样性的倒数。凋落物层和土壤层中不同相对多度土壤动物的丰富度也具有幂律尺度推绎规律。凋落物层和土壤层中土壤动物多样性格局都具有多重分形特征,但凋落物层中多样性的分形结构比土壤层更均匀,且两层间优势类群与稀有类群的尺度推绎特征在多重分形谱上不同格局。幂律尺度推绎规律对于有着较高丰富度与多度的土壤动物同样存在,从而有助于揭示地下生物多样性的空间分布机制。     

Distance to the Scaling Law: A Useful Approach for Unveiling Relationships between Crime and Urban Metrics

We report on a quantitative analysis of relationships between the number of homicides, population size and ten other urban metrics. By using data from Brazilian cities, we show that well-defined average scaling laws with the population size emerge when investigating the relations between population and number of homicides as well as population and urban metrics. We also show that the fluctuations around the scaling laws are log-normally distributed, which enabled us to model these scaling laws by a stochastic-like equation driven by a multiplicative and log-normally distributed noise. Because of the scaling laws, we argue that it is better to employ logarithms in order to describe the number of homicides in function of the urban metrics via regression analysis. In addition to the regression analysis, we propose an approach to correlate crime and urban metrics via the evaluation of the distance between the actual value of the number of homicides (as well as the value of the urban metrics) and the value that is expected by the scaling law with the population size. This approach has proved to be robust and useful for unveiling relationships/behaviors that were not properly carried out by the regression analysis, such as the non-explanatory potential of the elderly population when the number of homicides is much above or much below the scaling law, the fact that unemployment has explanatory potential only when the number of homicides is considerably larger than the expected by the power law, and a gender difference in number of homicides, where cities with female population below the scaling law are characterized by a number of homicides above the power law.     

Universal scaling of the C--G distribution of genes

Using our previous result that the C--G distribution in genomes is very broad, varying as a power law of the size of the block of genome considered, we examine the C--G distribution in genes themselves. We show that the widths of the C--G distributions for the genes of several simple organisms also vary as power laws. This suggests that the power law behavior gives a universal scaling whereby the distributions for the C--G content of the genes from all species are mapped onto a single function.     

Reproductive correlation and mean-variance scaling of reproductive output for a forest model

We analyse the mean-variance scaling of reproductive output for a previously published forest model. The model relates individual reproductive effort and pollen limitation to the degree of synchrony in reproduction throughout a forest. We show that the exponent of Taylor's power law reflects the degree of synchrony of reproduction because it indicates the covariance of reproductive behavior. Further, we are able to relate the three components of masting, individual variability, population variability and synchrony in reproductive output, using Taylor's power law. Therefore Taylor's power law can be used as a synoptic index of masting.     

Comparative power law analysis for the spatial heterogeneity scaling of the hot‐spring microbiomes

Spatial heterogeneity is a fundamental property of any natural ecosystems, including hot spring and human microbiomes. Two important scales that spatial heterogeneity exhibits are population and community scales, and Taylor's power law (PL) and its extensions (PLEs) offer ideal quantitative models to assess population‐ and community‐level heterogeneities. Here we analyse 165 hot spring microbiome samples at the global scale that cover a wide range of temperatures (7.5–99°C) and pH levels (3.3–9). We explore a question of fundamental importance for measuring the spatial heterogeneity of the hot‐spring microbiome and further discuss their ecological implications: How do critical environmental factors such as temperature and pH influence the scaling of community spatial heterogeneity? We are particularly interested in the existence of a universal scaling model that is independent of environmental gradients. By applying PL and PLEs, we were able to obtain such scaling parameters of the hot spring at both community and population levels, which are temperature‐ and pH‐invariant. These findings suggest that while the hot‐spring microbiomes located at different regions may have different environmental conditions, they share a fundamental heterogeneity scaling parameter, analogically similar to the gravitational acceleration on Earth, which may vary slightly depending on altitude and latitude, but is invariant overall. In contrast, similar to the physics of the Moon and Earth, which have different gravitational accelerations, the hot spring and human microbiomes can have different scaling parameters as demonstrated in this study.     

Community fluctuations and local extinction in a planktonic food web

Determining statistical patterns irrespective of interacting agents (i.e. macroecology) is useful to explore the mechanisms driving population fluctuations and extinctions in natural food webs. Here, we tested four predictions of a neutral model on the distribution of community fluctuations (CF) and the distributions of persistence times (APT). Novel predictions for the food web were generated by combining (1) body size–density scaling, (2) Taylor's law and (3) low efficiency of trophic transference. Predictions were evaluated on an exceptional data set of plankton with 15 years of weekly samples encompassing c. 250 planktonic species from three trophic levels, sampled in the western English Channel. Highly symmetric non‐Gaussian distributions of CF support zero‐sum dynamics. Variability in CF decreased while a change from an exponential to a power law distribution of APT from basal to upper trophic positions was detected. Results suggest a predictable but profound effect of trophic position on fluctuations and extinction in natural communities.     

Scaling behavior and structure of denatured proteins

An ensemble of random-coil conformations with no persistent structures has long been accepted as the classical model of denatured proteins due to its consistency with the experimentally determined scaling of protein sizes. However, recent NMR spectroscopy studies on proteins at high chemical denaturant concentrations suggest the presence of significant amounts of native-like structures, in contrast to the classical random-coil picture. To reconcile these seemingly controversial observations, we examine thermally denatured states of experimentally characterized proteins by using molecular dynamics simulations. For all studied proteins, we find that denatured states indeed have strong local conformational bias toward native states while a random-coil power law scaling of protein sizes is preserved. In addition, we explain why experimentally determined size of the protein creatine kinase does not follow general scaling. In simulations, we observe that this protein exhibits a stable intermediate state, the size of which is consistent with the reported experimental observation.     

Scaling of differentiation in networks: nervous systems,organisms, ant colonies,ecosystems, businesses,universities, cities,electronic circuits,and Legos

Nodes in networks are often of different types, and in this sense networks are differentiated. Here we examine the relationship between network differentiation and network size in networks under economic or natural selective pressure, such as electronic circuits (networks of electronic components), Legos (networks of Lego pieces), businesses (networks of employees), universities (networks of faculty), organisms (networks of cells), ant colonies (networks of ants), and nervous systems (networks of neurons). For each of these we find that (i) differentiation increases with network size, and (ii) the relationship is consistent with a power law. These results are explained by a hypothesis that, because nodes are costly to build and maintain in such "selected networks", network size is optimized, and from this the power-law relationship may be derived. The scaling exponent depends on the particular kind of network, and is determined by the degree to which nodes are used in a combinatorial fashion to carry out network-level functions. We find that networks under natural selection (organisms, ant colonies, and nervous systems) have much higher combinatorial abilities than the networks for which human ingenuity is involved (electronic circuits, Legos, businesses, and universities). A distinct but related optimization hypothesis may be used to explain scaling of differentiation in competitive networks (networks where the nodes themselves, rather than the entire network, are under selective pressure) such as ecosystems (networks of organisms).     

Inferring the Rate-Length Law of Protein Folding

We investigate the rate-length scaling law of protein folding, a key undetermined scaling law in the analytical theory of protein folding. Available data yield statistically significant evidence for the existence of a rate-length law capable of predicting folding times to within about two orders of magnitude (over 9 decades of variation). Unambiguous determination of the functional form of such a law could provide key mechanistic insight into folding. Four proposed laws from literature (power law, exponential, and two stretched exponentials) are tested against one another, and it is found that the power law best explains the data by a modest margin. We conclude that more data is necessary to unequivocally infer the rate-length law. Such data could be obtained through a small number of protein folding experiments on large protein domains.     

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background

We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links.

Results

The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' – the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections – influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment.

Conclusion

A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

     

A topologically related singularity suggests a maximum preferred size for protein domains

A variety of protein physicochemical as well as topological properties, demonstrate a scaling behavior relative to chain length. Many of the scalings can be modeled as a power law which is qualitatively similar across the examples. In this article, we suggest a rational explanation to these observations on the basis of both protein connectivity and hydrophobic constraints of residues compactness relative to surface volume. Unexpectedly, in an examination of these relationships, a singularity was shown to exist near 255-270 residues length, and may be associated with an upper limit for domain size. Evaluation of related G-factor data points to a wide range of conformational plasticity near this point. In addition to its theoretical importance, we show by an application of CASP experimental and predicted structures, that the scaling is a practical filter for protein structure prediction.     

Power-law species–area relationships and self-similar species distributions within finite areas

The species–area relationship (SAR) is often expressed as a power law, which indicates scale invariance. It has been claimed that the scale invariance – or self‐similarity at the community level – is not compatible with the self‐similarity at the level of spatial distribution of individual species, because the power law would only emerge if distributions for all species had identical fractal dimensions (FD). Here we show that even if species differ in their FD, the resulting SAR is approximately linear on a log–log scale because observed spatial distributions are inevitably spatially restricted – a phenomenon we term the ‘finite‐area effect’. Using distribution atlases, we demonstrate that the apparent power law of SARs for central European birds is attributable to this finite‐area effect affecting species that indeed reveal self‐similar distributions. We discuss implications of this mechanism producing the SAR.     

Sanio's laws revisited. Size-dependent changes in the xylem architecture of trees

Early observations led Sanio [ Wissen. Bot. , 8 , (1872) 401] to state that xylem conduit diameters and lengths in a coniferous tree increase from the apex down to a height below which they begin to decrease towards the tree base. Sanio's law of vertical tapering has been repeatedly tested with contradictory results and the debate over the scaling of conduit diameters with distance from the apex has not been settled. The debate has recently acquired new vigour, as an accurate knowledge of the vertical changes in wood anatomy has been shown to be crucial to scaling metabolic properties to plant and ecosystem levels. Contrary to Sanio's hypothesis, a well known model (MST, metabolic scaling theory) assumes that xylem conduits monotonically increase in diameter with distance from the apex following a power law. This has been proposed to explain the three-fourth power scaling between size and metabolism seen across plants. Here, we (i) summarized available data on conduit tapering in trees and (ii) propose a new numerical model that could explain the observed patterns. Data from 101 datasets grouped into 48 independent profiles supported the notions that phylogenetic group (angiosperms versus gymnosperms) and tree size strongly affected the vertical tapering of conduit diameter. For both angiosperms and gymnosperms, within-tree tapering also varied with distance from the apex. The model (based on the concept that optimal conduit tapering occurs when the difference between photosynthetic gains and wall construction costs is maximal) successfully predicted all three major empirical patterns. Our results are consistent with Sanio's law only for large trees and reject the MST assumptions that vertical tapering in conduit diameter is universal and independent of rank number.     

Do generalized scaling laws exist for species abundance distribution in mountains?

Knowing the global pattern of species diversity is a central goal of the science of ecology, and scaling laws can be useful for analysis of cross-scale biodiversity patterns. An elevational gradient in a warm temperate zone of the Donglingshan mountains (China) is used to test the scaling laws of species abundance distribution using multifractal analysis. We show that the power law scaling relationship holds for not just the classical SAR (species–area relationship for richness), but also for Shannon and Simpson diversity. In fact, we find power-laws in the generalized species abundance distribution at all stratal levels of the forest (trees, shrubs and herbs). The fact that these laws exist across a heterogeneous landscape representing a strong bioclimatic gradient suggests that biodiversity scaling laws may be more robust than previously thought.     

Self-similarity in species–area relationship and in species abundance distribution

In an influential paper, Harte et al. highlighted the scaling or 'self-similar' character of the power law species–area relationship (SAR), and used this feature to derive a species abundance distribution (SAD) and an endemics–area relationship. Here I show that their analysis was incorrect and leads to unrealistic results. I develop a different approach and obtain different results, both for SAD and for endemism. In particular, I show that the power law SAR is naturally associated with the power law statistical distribution, which is the only self-similar distribution and closely matches empirical SADs. The results in this paper shed light on some of the main issues that have been discussed with regard to SARs: their relationship with the lognormal and with the neutral theory, the relative importance of sampling effects vs other mechanisms, and the deviations from a power law. The equations that I develop are simple and easy to apply to field studies.