Kleiber's law

Last updated
Kleiber's plot comparing body size to metabolic rate for a variety of species. Kleiber1947.svg
Kleiber's plot comparing body size to metabolic rate for a variety of species.

Kleiber's law, named after Max Kleiber for his biology work in the early 1930s, states, after many observation that, for a vast number of animals, an animal's Basal Metabolic Rate scales to the 34 power of the animal's mass. [2]

Contents

More precisely : posing w = mass of the animal in kilograms, then BMR = 70w kilocalories per day, or BMR = 3.4w watts. [3]

Thus, over the same time span, a cat having a mass 100 times that of a mouse will consume only about 32 times the energy the mouse uses.

Presently is unclear if the value of the exponent in Kleiber's law is correct, in part because the law currently lacks a single theoretical explanation that is entirely satisfactory.

More recently, Kleiber's law has also been shown to apply in plants, [4] suggesting that Kleiber's observation is much more general

Proposed explanations for the law

Kleiber's law, like many other biological allometric laws, is a consequence of the physics and/or geometry of circulatory systems in biology. [5] Max Kleiber first discovered the law when analyzing a large number of independent studies on respiration within individual species. [2] Kleiber expected to find an exponent of 23 (for reasons explained below), and was confounded by the discovery of a 34 exponent.

Historical context and the 23 scaling surface law

Before Kleiber's observation of the 3/4 power scaling, a 2/3 power scaling was largely anticipated based on the "surface law", [6] which states that the basal metabolism of animals differing in size is nearly proportional to their respective body surfaces. This surface law reasoning originated from simple geometrical considerations. As organisms increase in size, their volume (and thus mass) increases at a much faster rate than their surface area. Explanations for 23-scaling tend to assume that metabolic rates scale to avoid heat exhaustion. Because bodies lose heat passively via their surface but produce heat metabolically throughout their mass, the metabolic rate must scale in such a way as to counteract the square–cube law. Because many physiological processes, like heat loss and nutrient uptake, were believed to be dependent on the surface area of an organism, it was hypothesized that metabolic rate would scale with the 2/3 power of body mass. [7] Rubner (1883) first demonstrated the law in accurate respiration trials on dogs. [8]

Kleiber's contribution

Max Kleiber challenged this notion in the early 1930s. Through extensive research on various animals' metabolic rates, he found that a 3/4 power scaling provided a better fit to the empirical data than the 2/3 power. [2] His findings provided the groundwork for understanding allometric scaling laws in biology, leading to the formulation of the Metabolic Scaling Theory and the later work by West, Brown, and Enquist, among others.

Such an argument does not address the fact that different organisms exhibit different shapes (and hence have different surface-area-to-volume ratios, even when scaled to the same size). Reasonable estimates for organisms' surface area do appear to scale linearly with the metabolic rate. [9]

Exponent 34

West, Brown, and Enquist, (hereafter WBE) proposed a general theory for the origin of many allometric scaling laws in biology. According to the WBE theory, 34-scaling arises because of efficiency in nutrient distribution and transport throughout an organism. In most organisms, metabolism is supported by a circulatory system featuring branching tubules (i.e., plant vascular systems, insect tracheae, or the human cardiovascular system). WBE claim that (1) metabolism should scale proportionally to nutrient flow (or, equivalently, total fluid flow) in this circulatory system and (2) in order to minimize the energy dissipated in transport, the volume of fluid used to transport nutrients (i.e., blood volume) is a fixed fraction of body mass. [10] The model assumes that the energy dissipated is minimized and that the terminal tubes do not vary with body size. It provides a complete analysis of numerous anatomical and physiological scaling relations for circulatory systems in biology that generally agree with data. [10] More generally, the model predicts the structural and functional properties of vertebrate cardiovascular and respiratory systems, plant vascular systems, insect tracheal tubes, and other distribution networks.

They then analyze the consequences of these two claims at the level of the smallest circulatory tubules (capillaries, alveoli, etc.). Experimentally, the volume contained in those smallest tubules is constant across a wide range of masses. Because fluid flow through a tubule is determined by the volume thereof, the total fluid flow is proportional to the total number of smallest tubules. Thus, if B denotes the basal metabolic rate, Q the total fluid flow, and N the number of minimal tubules, Circulatory systems do not grow by simply scaling proportionally larger; they become more deeply nested. The depth of nesting depends on the self-similarity exponents of the tubule dimensions, and the effects of that depth depend on how many "child" tubules each branching produces. Connecting these values to macroscopic quantities depends (very loosely) on a precise model of tubules. WBE show that if the tubules are well-approximated by rigid cylinders, then, to prevent the fluid from "getting clogged" in small cylinders, the total fluid volume V satisfies [11] (Despite conceptual similarities, this condition is inconsistent with Murray's law) [12] Because blood volume is a fixed fraction of body mass, [10]

Non-power-law scaling

The WBE theory predicts that the scaling of metabolism is not a strict power law but rather should be slightly curvilinear. The 3/4 exponent only holds exactly in the limit of organisms of infinite size. As body size increases, WBE predict that the scaling of metabolism will converge to a ~3/4 scaling exponent. [10] Indeed, WBE predicts that the metabolic rates of the smallest animals tend to be greater than expected from the power-law scaling (see Fig. 2 in Savage et al. 2010 [13] ). Further, Metabolic rates for smaller animals (birds under 10 kg [22 lb], or insects) typically fit to 23 much better than 34; for larger animals, the reverse holds. [14] As a result, log-log plots of metabolic rate versus body mass can "curve" slightly upward, and fit better to quadratic models. [15] In all cases, local fits exhibit exponents in the [23,34] range. [16]

Elaborated and Modified circulatory models

Elaborations of the WBE model predict larger scaling exponents, worsening the discrepancy with observed data. [17] see also, [14] [18] ). However, one can retain a similar theory by relaxing WBE's assumption of a nutrient transport network that is both fractal and circulatory. Different networks are less efficient in that they exhibit a lower scaling exponent. Still, a metabolic rate determined by nutrient transport will always exhibit scaling between 23 and 34. [16] WBE argued that fractal-like circulatory networks are likely under strong stabilizing selection to evolve to minimize energy used for transport. If selection for greater metabolic rates is favored, then smaller organisms will prefer to arrange their networks to scale as 23. Still, selection for larger-mass organisms will tend to result in networks that scale as 34, which produces the observed curvature. [19]

Modified thermodynamic models

An alternative model notes that metabolic rate does not solely serve to generate heat. Metabolic rate contributing solely to useful work should scale with power 1 (linearly), whereas metabolic rate contributing to heat generation should be limited by surface area and scale with power 23. Basal metabolic rate is then the convex combination of these two effects: if the proportion of useful work is f, then the basal metabolic rate should scale as where k and k are constants of proportionality. k in particular describes the surface area ratio of organisms and is approximately 0.1 kJ·h−1·g−2/3; [20] typical values for f are 15-20%. [21] The theoretical maximum value of f is 21%, because the efficiency of glucose oxidation is only 42%, and half of the ATP so produced is wasted. [20]

Criticism of explanations

Kozłowski and Konarzewski have argued against attempts to explain Kleiber's law via any sort of limiting factor because metabolic rates vary by factors of 4-5 between rest and activity. Hence, any limits that affect the scaling of the basal metabolic rate would make elevated metabolism — and hence all animal activity — impossible. [22] WBE conversely argue that natural selection can indeed select for minimal transport energy dissipation during rest, without abandoning the ability for less efficient function at other times. [23]

Other researchers have also noted that Kozłowski and Konarzewski's criticism of the law tends to focus on precise structural details of the WBE circulatory networks but that the latter are not essential to the model. [11]

Experimental support

Analyses of variance for a variety of physical variables suggest that although most variation in basal metabolic rate is determined by mass, additional variables with significant effects include body temperature and taxonomic order. [24] [25]

A 1932 work by Brody calculated that the scaling was approximately 0.73. [9] [26]

A 2004 analysis of field metabolic rates for mammals conclude that they appear to scale with exponent 0.749. [19]

Generalizations

Kleiber's law has been reported to interspecific comparisons and has been claimed not to apply at the intraspecific level. [27] The taxonomic level that body mass metabolic allometry should be studied has been debated [28] [29] Nonetheless, several analyses suggest that while the exponents of the Kleiber's relationship between body size and metabolism can vary at the intraspecific level, statistically, intraspecific exponents in both plants and animals tend to cluster around 3/4. [30]

In other kingdoms

A 1999 analysis concluded that biomass production in a given plant scaled with the 34 power of the plant's mass during the plant's growth, [31] but a 2001 paper that included various types of unicellular photosynthetic organisms found scaling exponents intermediate between 0.75 and 1.00. [32] Similarly, a 2006 paper in Nature argued that the exponent of mass is close to 1 for plant seedlings, but that variation between species, phyla, and growth conditions overwhelm any "Kleiber's law"-like effects. [33] But, metabolic scaling theory can successfully resolve these apparent exceptions and deviations. For finite-size corrections in networks with both area-preserving and area-increasing branching, the WBE model predicts that fits to data for plants yield scaling exponents that are steeper than 3/4 in small plants but then converge to 3/4 in larger plants (see [34] [17] ).

Intra-organismal results

Because cell protoplasm appears to have constant density across a range of organism masses, a consequence of Kleiber's law is that, in larger species, less energy is available to each cell volume. Cells appear to cope with this difficulty via choosing one of the following two strategies: smaller cells or a slower cellular metabolic rate. Neurons and adipocytes exhibit the former; every other type of cell, the latter. [35] As a result, different organs exhibit different allometric scalings (see table). [9]

Allometric scalings for BMR-vs.-mass in human tissue
OrganScaling exponent
Brain0.7
Kidney0.85
Liver0.87
Heart0.98
Muscle1.0
Skeleton1.1

See also

Related Research Articles

<span class="mw-page-title-main">Metabolism</span> Set of chemical reactions in organisms

Metabolism is the set of life-sustaining chemical reactions in organisms. The three main functions of metabolism are: the conversion of the energy in food to energy available to run cellular processes; the conversion of food to building blocks of proteins, lipids, nucleic acids, and some carbohydrates; and the elimination of metabolic wastes. These enzyme-catalyzed reactions allow organisms to grow and reproduce, maintain their structures, and respond to their environments. The word metabolism can also refer to the sum of all chemical reactions that occur in living organisms, including digestion and the transportation of substances into and between different cells, in which case the above described set of reactions within the cells is called intermediary metabolism.

<span class="mw-page-title-main">Herbivore</span> Organism that eats mostly or exclusively plant material

A herbivore is an animal anatomically and physiologically evolved to feed on plants, especially upon vascular tissues such as foliage, fruits or seeds, as the main component of its diet. These more broadly also encompass animals that eat non-vascular autotrophs such as mosses, algae and lichens, but do not include those feeding on decomposed plant matters or macrofungi.

<span class="mw-page-title-main">Excretion</span> Elimination by an organism of metabolic waste products

Excretion is elimination of metabolic waste, which is an essential process in all organisms. In vertebrates, this is primarily carried out by the lungs, kidneys, and skin. This is in contrast with secretion, where the substance may have specific tasks after leaving the cell. For example, placental mammals expel urine from the bladder through the urethra, which is part of the excretory system. Unicellular organisms discharge waste products directly through the surface of the cell.

The metabolic theory of ecology (MTE) is the ecological component of the more general Metabolic Scaling Theory and Kleiber's law. It posits that the metabolic rate of organisms is the fundamental biological rate that governs most observed patterns in ecology. MTE is part of a larger set of theory known as metabolic scaling theory that attempts to provide a unified theory for the importance of metabolism in driving pattern and process in biology from the level of cells all the way to the biosphere.

The oxygen minimum zone (OMZ), sometimes referred to as the shadow zone, is the zone in which oxygen saturation in seawater in the ocean is at its lowest. This zone occurs at depths of about 200 to 1,500 m (700–4,900 ft), depending on local circumstances. OMZs are found worldwide, typically along the western coast of continents, in areas where an interplay of physical and biological processes concurrently lower the oxygen concentration and restrict the water from mixing with surrounding waters, creating a "pool" of water where oxygen concentrations fall from the normal range of 4–6 mg/L to below 2 mg/L.

Metabolic ecology is a field of ecology aiming to understand constraints on metabolic organization as important for understanding almost all life processes. Main focus is on the metabolism of individuals, emerging intra- and inter-specific patterns, and the evolutionary perspective.

<span class="mw-page-title-main">Allometry</span> Study of the relationship of body size to shape, anatomy, physiology, and behavior

Allometry is the study of the relationship of body size to shape, anatomy, physiology and behaviour, first outlined by Otto Snell in 1892, by D'Arcy Thompson in 1917 in On Growth and Form and by Julian Huxley in 1932.

The dynamic energy budget (DEB) theory is a formal metabolic theory which provides a single quantitative framework to dynamically describe the aspects of metabolism of all living organisms at the individual level, based on assumptions about energy uptake, storage, and utilization of various substances. The DEB theory adheres to stringent thermodynamic principles, is motivated by universally observed patterns, is non-species specific, and links different levels of biological organization as prescribed by the implications of energetics. Models based on the DEB theory have been successfully applied to over 1000 species with real-life applications ranging from conservation, aquaculture, general ecology, and ecotoxicology. The theory is contributing to the theoretical underpinning of the emerging field of metabolic ecology.

<span class="mw-page-title-main">Metabolic network modelling</span> Form of biological modelling

Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology.

<span class="mw-page-title-main">Geoffrey West</span> British Theoretical Physicist

Geoffrey Brian West is a British theoretical physicist and former president and distinguished professor of the Santa Fe Institute. He is one of the leading scientists working on a scientific model of cities. Among other things, his work states that with the doubling of a city's population, salaries per capita will generally increase by 15%.

<span class="mw-page-title-main">Evolutionary physiology</span> Study of evolutionary changes in physiological characteristics

Evolutionary physiology is the study of the biological evolution of physiological structures and processes; that is, the manner in which the functional characteristics of organisms have responded to natural selection or sexual selection or changed by random genetic drift across multiple generations during the history of a population or species. It is a sub-discipline of both physiology and evolutionary biology. Practitioners in the field come from a variety of backgrounds, including physiology, evolutionary biology, ecology, and genetics.

<span class="mw-page-title-main">Tree allometry</span> Quantitative relations between some key characteristic dimensions of trees

Tree allometry establishes quantitative relations between some key characteristic dimensions of trees and other properties. To the extent these statistical relations, established on the basis of detailed measurements on a small sample of typical trees, hold for other individuals, they permit extrapolations and estimations of a host of dendrometric quantities on the basis of a single measurements.

<span class="mw-page-title-main">Deep-sea gigantism</span> Tendency for deep-sea species to be larger than their shallower-water relatives

In zoology, deep-sea gigantism or abyssal gigantism is the tendency for species of deep-sea dwelling animals to be larger than their shallower-water relatives across a large taxonomic range. Proposed explanations for this type of gigantism include necessary adaptation to colder temperature, food scarcity, reduced predation pressure and increased dissolved oxygen concentrations in the deep sea. The harsh conditions and inhospitality of the underwater environment in general, as well as the inaccessibility of the abyssal zone for most human-made underwater vehicles, have hindered the study of this topic.

In biophysical fluid dynamics, Murray's law is a potential relationship between radii at junctions in a network of fluid-carrying tubular pipes. Its simplest version proposes that whenever a branch of radius splits into two branches of radii and , then the three radii should obey the equation If network flow is smooth and leak-free, then systems that obey Murray's law minimize the resistance to flow through the network. For turbulent networks, the law takes the same form but with a different characteristic exponent α.

Max Kleiber was a Swiss agricultural biologist, born and educated in Zürich, Switzerland.

<span class="mw-page-title-main">Rate-of-living theory</span> Theory of biological ageing

The rate of living theory postulates that the faster an organism’s metabolism, the shorter its lifespan. First proposed by Max Rubner in 1908, the theory was based on his observation that smaller animals had faster metabolisms and shorter lifespans compared to larger animals with slower metabolisms. The theory gained further credibility through the work of Raymond Pearl, who conducted experiments on drosophila and cantaloupe seeds, which supported Rubner's initial observation. Pearl's findings were later published in his book, The Rate of Living, in 1928, in which he expounded upon Rubner's theory and demonstrated a causal relationship between the slowing of metabolism and an increase in lifespan.

Brian Joseph Enquist is an American biologist and academic. Enquist is a professor of biology at the University of Arizona. He is also external professor at the Santa Fe Institute. He is a biologist, plant biologist and an ecologist. He was elected as a Fellow of the American Association for the Advancement of Science (AAAS) in 2012 and the Ecological Society of America (ESA) in 2018.

<span class="mw-page-title-main">Biological rules</span> Generalized principle to describe patterns observed in living organisms

A biological rule or biological law is a generalized law, principle, or rule of thumb formulated to describe patterns observed in living organisms. Biological rules and laws are often developed as succinct, broadly applicable ways to explain complex phenomena or salient observations about the ecology and biogeographical distributions of plant and animal species around the world, though they have been proposed for or extended to all types of organisms. Many of these regularities of ecology and biogeography are named after the biologists who first described them.

<span class="mw-page-title-main">Jarman–Bell principle</span> Ecological concept linking an herbivores diet and size

The Jarman–Bell principle is a concept in ecology that the food quality of a herbivore's intake decreases as the size of the herbivore increases, but the amount of such food increases to counteract the low quality foods. It operates by observing the allometric properties of herbivores. The principle was coined by P.J Jarman (1968.) and R.H.V Bell (1971).

Plant growth analysis refers to a set of concepts and equations by which changes in size of plants over time can be summarised and dissected in component variables. It is often applied in the analysis of growth of individual plants, but can also be used in a situation where crop growth is followed over time.

References

  1. Kleiber M (October 1947). "Body size and metabolic rate". Physiological Reviews. 27 (4): 511–41. doi:10.1152/physrev.1947.27.4.511. PMID   20267758.
  2. 1 2 3 Kleiber M (January 1932). "Body size and metabolism". Hilgardia. 6 (11): 315–353. doi: 10.3733/hilg.v06n11p315 .
  3. Smil V (2000). "Laying down the law". Nature. 403 (6770): 597. Bibcode:2000Natur.403..597S. doi:10.1038/35001159. PMID   10688176.
  4. Enquist BJ, Brown JH, West GB (1998). "Allometric scaling of plant energetics and population density". Nature. 395 (10): 163–165. Bibcode:1998Natur.395..163E. doi:10.1038/25977.
  5. Schmidt-Nielsen K (1984). Scaling: Why is animal size so important? . NY, NY: Cambridge University Press. ISBN   978-0521266574.
  6. Harris JA, Benedict, FG (1919). "A biometric study of basal metabolism in man". Carnegie Inst. Of Wash . 6 (279): 31–266.
  7. Thompson, D. W. (1917). On Growth and Form. Cambridge University Press.
  8. Rubner M (1883). "Über den Einfluss der Körpergrosse auf Stoff- und Kraftwechsel". Zeitschr. F. BioI. 19: 535–562.
  9. 1 2 3 Hulbert AJ (28 April 2014). "A Sceptics View: "Kleiber's Law" or the "3/4 Rule" is neither a Law nor a Rule but Rather an Empirical Approximation". Systems. 2 (2): 186–202. doi: 10.3390/systems2020186 .
  10. 1 2 3 4 West GB, Brown JH, Enquist BJ (April 1997). "A general model for the origin of allometric scaling laws in biology". Science. 276 (5309): 122–6. doi:10.1126/science.276.5309.122. PMID   9082983. S2CID   3140271.
  11. 1 2 Etienne RS, Apol ME, Olff HA (2006). "Demystifying the West, Brown & Enquist model of the allometry of metabolism". Functional Ecology. 20 (2): 394–399. Bibcode:2006FuEco..20..394E. doi: 10.1111/j.1365-2435.2006.01136.x .
  12. Painter PR, Edén P, Bengtsson HU (August 2006). "Pulsatile blood flow, shear force, energy dissipation and Murray's Law". Theoretical Biology & Medical Modelling. 3 (1): 31. doi: 10.1186/1742-4682-3-31 . PMC   1590016 . PMID   16923189.
  13. West GB, Brown JH, Enquist BJ (2008). "Sizing Up Allometric Scaling Theory". PLOS Comput Biol. 4 (9): 122–126. doi:10.1126/science.276.5309.122. PMID   9082983. S2CID   3140271.
  14. 1 2 Dodds PS, Rothman DH, Weitz JS (March 2001). "Re-examination of the "3/4-law" of metabolism". Journal of Theoretical Biology. 209 (1): 9–27. arXiv: physics/0007096 . Bibcode:2001JThBi.209....9D. doi:10.1006/jtbi.2000.2238. PMID   11237567. S2CID   9168199.
  15. Kolokotrones T, Deeds EJ, Fontana W (April 2010). "Curvature in metabolic scaling". Nature. 464 (7289): 753–6. Bibcode:2010Natur.464..753K. doi:10.1038/nature08920. PMID   20360740. S2CID   4374163.
    But note that a quadratic curve has undesirable theoretical implications; see MacKay NJ (July 2011). "Mass scale and curvature in metabolic scaling. Comment on: T. Kolokotrones et al., curvature in metabolic scaling, Nature 464 (2010) 753-756". Journal of Theoretical Biology. 280 (1): 194–6. Bibcode:2011JThBi.280..194M. doi:10.1016/j.jtbi.2011.02.011. PMID   21335012.
  16. 1 2 Banavar JR, Moses ME, Brown JH, Damuth J, Rinaldo A, Sibly RM, Maritan A (September 2010). "A general basis for quarter-power scaling in animals". Proceedings of the National Academy of Sciences of the United States of America. 107 (36): 15816–20. Bibcode:2010PNAS..10715816B. doi: 10.1073/pnas.1009974107 . PMC   2936637 . PMID   20724663.
  17. 1 2 Savage VM, Deeds EJ, Fontana W (September 2008). "Sizing up allometric scaling theory". PLOS Computational Biology. 4 (9): e1000171. Bibcode:2008PLSCB...4E0171S. doi: 10.1371/journal.pcbi.1000171 . PMC   2518954 . PMID   18787686.
  18. Apol ME, Etienne RS, Olff H (2008). "Revisiting the evolutionary origin of allometric metabolic scaling in biology". Functional Ecology. 22 (6): 1070–1080. Bibcode:2008FuEco..22.1070A. doi: 10.1111/j.1365-2435.2008.01458.x .
  19. 1 2 Savage VM, Gillooly JF, Woodruff WH, West GB, Allen AP, Enquist BJ, Brown JH (April 2004). "The predominance of quarter-power scaling in biology". Functional Ecology. 18 (2): 257–282. Bibcode:2004FuEco..18..257S. doi: 10.1111/j.0269-8463.2004.00856.x . The original paper by West et al. (1997), which derives a model for the mammalian arterial system, predicts that smaller mammals should show consistent deviations in the direction of higher metabolic rates than expected from M34 scaling. Thus, metabolic scaling relationships are predicted to show a slight curvilinearity at the smallest size range.
  20. 1 2 Ballesteros FJ, Martinez VJ, Luque B, Lacasa L, Valor E, Moya A (January 2018). "On the thermodynamic origin of metabolic scaling". Scientific Reports. 8 (1): 1448. Bibcode:2018NatSR...8.1448B. doi:10.1038/s41598-018-19853-6. PMC   5780499 . PMID   29362491.
  21. Zotin AI (1990). Thermodynamic Bases of Biological Processes: Physiological Reactions and Adaptations. Walter de Gruyter. ISBN   9783110114010.
  22. Kozlowski J, Konarzewski M (2004). "Is West, Brown and Enquist's model of allometric scaling mathematically correct and biologically relevant?". Functional Ecology. 18 (2): 283–9. Bibcode:2004FuEco..18..283K. doi: 10.1111/j.0269-8463.2004.00830.x .
  23. Brown JH, West GB, Enquist BJ (2005). "Yes, West, Brown and Enquist's model of allometric scaling is both mathematically correct and biologically relevant". Functional Ecology. 19 (4): 735–738. doi: 10.1111/j.1365-2435.2005.01022.x .
  24. Clarke A, Rothery P, Isaac NJ (May 2010). "Scaling of basal metabolic rate with body mass and temperature in mammals". The Journal of Animal Ecology. 79 (3): 610–9. Bibcode:2010JAnEc..79..610C. doi: 10.1111/j.1365-2656.2010.01672.x . PMID   20180875.
  25. Hayssen V, Lacy RC (1985). "Basal metabolic rates in mammals: taxonomic differences in the allometry of BMR and body mass". Comparative Biochemistry and Physiology. A, Comparative Physiology. 81 (4): 741–54. doi:10.1016/0300-9629(85)90904-1. PMID   2863065.
  26. Brody S (1945). Bioenergetics and Growth. NY, NY: Reinhold.
  27. Heusner AA (1982-04-01). "Energy metabolism and body size I. Is the 0.75 mass exponent of Kleiber's equation a statistical artifact?". Respiration Physiology. 48 (1): 1–12. doi:10.1016/0034-5687(82)90046-9. ISSN   0034-5687. PMID   7111915.
  28. White CR, Blackburn TM, Seymour RS (October 2009). "Phylogenetically informed analysis of the allometry of Mammalian Basal metabolic rate supports neither geometric nor quarter-power scaling". Evolution; International Journal of Organic Evolution. 63 (10): 2658–67. doi: 10.1111/j.1558-5646.2009.00747.x . PMID   19519636. S2CID   16889020.
  29. Sieg AE, O'Connor MP, McNair JN, Grant BW, Agosta SJ, Dunham AE (November 2009). "Mammalian metabolic allometry: do intraspecific variation, phylogeny, and regression models matter?". The American Naturalist. 174 (5): 720–33. doi:10.1086/606023. PMID   19799501. S2CID   36468932.
  30. Moses ME, Hou C, Woodruff WH, West GB, Nekola JC, Zuo W, Brown JH (2008). "Revisiting a model of ontogenetic growth: estimating model parameters from theory and data". The American Naturalist. 171 (5): 632–645. doi:10.1086/587073. PMID   18419571.
  31. Enquist BJ, West GB, Charnov EL, Brown JH (28 October 1999). "Allometric scaling of production and life-history variation in vascular plants". Nature. 401 (6756): 907–911. Bibcode:1999Natur.401..907E. doi:10.1038/44819. ISSN   1476-4687. S2CID   4397261.
    Corrigendum published 7 December 2000.
  32. Niklas KJ (2006). "A phyletic perspective on the allometry of plant biomass-partitioning patterns and functionally equivalent organ-categories". The New Phytologist. 171 (1): 27–40. doi: 10.1111/j.1469-8137.2006.01760.x . PMID   16771980.
  33. Reich PB, Tjoelker MG, Machado JL, Oleksyn J (January 2006). "Universal scaling of respiratory metabolism, size and nitrogen in plants". Nature. 439 (7075): 457–61. Bibcode:2006Natur.439..457R. doi:10.1038/nature04282. hdl: 11299/176835 . PMID   16437113. S2CID   1484450.
  34. Enquist BJ, Allen AP, Brown JH, Gillooly JF, Kerkhoff AJ, Niklas KJ, Price CA, West GB (February 2007). "Biological scaling: does the exception prove the rule?". Nature. 445 (7127): E9–10, discussion E10–1. Bibcode:2007Natur.445....9E. doi:10.1038/nature05548. PMID   17268426. S2CID   43905935. and associated responses
  35. Savage VM, Allen AP, Brown JH, Gillooly JF, Herman AB, Woodruff WH, West GB (March 2007). "Scaling of number, size, and metabolic rate of cells with body size in mammals". Proceedings of the National Academy of Sciences of the United States of America. 104 (11): 4718–23. Bibcode:2007PNAS..104.4718S. doi: 10.1073/pnas.0611235104 . PMC   1838666 . PMID   17360590.

Further reading