scholarly journals An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

2014 ◽  
Author(s):  
Charles Frasier
Keyword(s):  
Metabolic Rate ◽  
Body Mass ◽  
Characteristic Length ◽  
Data Set ◽  
Mass Data ◽  
Skeletal Musculature ◽  
Length Data ◽  
Unexplained Variance ◽  
Parameter Values ◽  
The Relationship

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE predicts the absolute value of Basal Metabolic Rate (BMR) for individual animals rather than parameters in the power law relationship BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measureable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set without any unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set without any unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals.

scholarly journals An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

2014 ◽  
Author(s):  
Charles Frasier
Keyword(s):  
Regression Analysis ◽  
Metabolic Rate ◽  
Body Mass ◽  
Characteristic Length ◽  
Data Set ◽  
Homogeneous Groups ◽  
Mass Data ◽  
Length Data ◽  
Unexplained Variance ◽  
The Relationship

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE deterministically predicts the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals. MMLE is thus distinct from other examinations of these topics that use species-averaged data to estimate the parameters in a statistically best fit power law relationship such as BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measurable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set with no error and thus no unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set with no error and thus no unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals. However for many of the addressed phylogenetic homogeneous groups, MMLE can predict the exponent obtained by regression analysis of the BMR and mass data using the exponent obtained by regression analysis of the mass and length data. This argues that MMLE may be able to accurately simultaneously predict BMR and mass for an individual animal.

scholarly journals An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

2014 ◽  
Author(s):  
Charles Frasier
Keyword(s):  
Regression Analysis ◽  
Metabolic Rate ◽  
Body Mass ◽  
Characteristic Length ◽  
Data Set ◽  
Homogeneous Groups ◽  
Mass Data ◽  
Length Data ◽  
Unexplained Variance ◽  
The Relationship

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE deterministically predicts the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals. MMLE is thus distinct from other examinations of these topics that use species-averaged data to estimate the parameters in a statistically best fit power law relationship such as BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measurable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set with no error and thus no unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set with no error and thus no unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals. However for many of the addressed phylogenetic homogeneous groups, MMLE can predict the exponent obtained by regression analysis of the BMR and mass data using the exponent obtained by regression analysis of the mass and length data. This argues that MMLE may be able to accurately simultaneously predict BMR and mass for an individual animal.

scholarly journals An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

2014 ◽  
Author(s):  
Charles Frasier
Keyword(s):  
Regression Analysis ◽  
Metabolic Rate ◽  
Body Mass ◽  
Characteristic Length ◽  
Data Set ◽  
Homogeneous Groups ◽  
Mass Data ◽  
Length Data ◽  
Unexplained Variance ◽  
The Relationship

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE deterministically predicts the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals. MMLE is thus distinct from other examinations of these topics that use species-averaged data to estimate the parameters in a statistically best fit power law relationship such as BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measurable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set with no error and thus no unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set with no error and thus no unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals. However for many of the addressed phylogenetic homogeneous groups, MMLE can predict the exponent obtained by regression analysis of the BMR and mass data using the exponent obtained by regression analysis of the mass and length data. This argues that MMLE may be able to accurately simultaneously predict BMR and mass for an individual animal.

scholarly journals The mass, metabolism and length explanation can simultaneously calculate an animal’s mass and metabolic rate from its characteristic length

2016 ◽  
Author(s):  
Charles C Frasier
Keyword(s):  
Metabolic Rate ◽  
Body Mass ◽  
Body Fat ◽  
Characteristic Length ◽  
Large Data ◽  
Data Set ◽  
Mass Data ◽  
Length Data ◽  
Using Data ◽  
The Impact

It is shown that the mass, metabolism and length explanation (MMLE) can simultaneously compute an animal’s body mass and BMR given its characteristic length using data for humans. MMLE was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. It was modernized in 2015 by explicitly treating dynamic similarity of mammals’ skeletal musculature and revising the treatment of BMR. Using two primary equations MMLE deterministically computes the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measureable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidea and a BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora were used to estimate values for the parameters occurring in the equations. With the estimated values MMLE can exactly compute every BMR and mass datum from the BMR and mass data set. Furthermore, MMLE can exactly compute every body mass datum from the mass and length data set. Since there is not a data set that simultaneously reports body mass, BMR and characteristic length for individual animals from the mammal orders that were analyzed it could not be determined whether or not MMLE could simultaneously compute both an animal’s BMR and body mass given its characteristic length. There are large data sets that report body mass, BMR and height for humans. A human’s characteristic length can be estimated from height. In this paper human data categorized by sex, age and body mass index (BMI) are used to show that MMLE can indeed simultaneously compute a human’s body mass and BMR given his or her characteristic length. The MMLE body mass equation is modified to explicitly address body fat because it appears that humans are fatter than other running/walking placental mammals. Differences in body fat seem to account for body mass and BMR sexual dimorphism among humans. The impact on BMR of the large and metabolically expensive human brain is addressed. Also mitochondria capability decline with age is addressed.

scholarly journals The mass, metabolism and length explanation can simultaneously calculate an animal’s mass and metabolic rate from its characteristic length

2016 ◽  
Author(s):  
Charles C Frasier
Keyword(s):  
Metabolic Rate ◽  
Body Mass ◽  
Body Fat ◽  
Characteristic Length ◽  
Large Data ◽  
Data Set ◽  
Mass Data ◽  
Length Data ◽  
Using Data ◽  
The Impact

It is shown that the mass, metabolism and length explanation (MMLE) can simultaneously compute an animal’s body mass and BMR given its characteristic length using data for humans. MMLE was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. It was modernized in 2015 by explicitly treating dynamic similarity of mammals’ skeletal musculature and revising the treatment of BMR. Using two primary equations MMLE deterministically computes the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measureable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidea and a BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora were used to estimate values for the parameters occurring in the equations. With the estimated values MMLE can exactly compute every BMR and mass datum from the BMR and mass data set. Furthermore, MMLE can exactly compute every body mass datum from the mass and length data set. Since there is not a data set that simultaneously reports body mass, BMR and characteristic length for individual animals from the mammal orders that were analyzed it could not be determined whether or not MMLE could simultaneously compute both an animal’s BMR and body mass given its characteristic length. There are large data sets that report body mass, BMR and height for humans. A human’s characteristic length can be estimated from height. In this paper human data categorized by sex, age and body mass index (BMI) are used to show that MMLE can indeed simultaneously compute a human’s body mass and BMR given his or her characteristic length. The MMLE body mass equation is modified to explicitly address body fat because it appears that humans are fatter than other running/walking placental mammals. Differences in body fat seem to account for body mass and BMR sexual dimorphism among humans. The impact on BMR of the large and metabolically expensive human brain is addressed. Also mitochondria capability decline with age is addressed.

scholarly journals Body mass data set for 1317 bird and 270 mammal species from Colombia

Ecology ◽  
2020 ◽  
Author(s):  
David Ocampo ◽  
Kevin G. Borja‐Acosta ◽  
Julián Lozano‐Flórez ◽  
Sebastián Cifuentes‐Acevedo ◽  
Enrique Arbeláez‐Cortés ◽  
...  
Keyword(s):  
Body Mass ◽  
Mammal Species ◽  
Data Set ◽  
Mass Data

scholarly journals The relationship between body mass and field metabolic rate among individual birds and mammals

2013 ◽  
Vol 82 (5) ◽  
pp. 1009-1020 ◽  
Author(s):  
Lawrence N. Hudson ◽  
Nick J. B. Isaac ◽  
Daniel C. Reuman
Keyword(s):  
Metabolic Rate ◽  
Body Mass ◽  
Field Metabolic Rate ◽  
The Relationship

The relationship between body mass and rate of rewarming from hibernation and daily torpor in mammals

1990 ◽  
Vol 151 (1) ◽  
pp. 349-359 ◽  
Author(s):  
F. Geiser ◽  
R. V. Baudinette
Keyword(s):  
Metabolic Rate ◽  
Body Mass ◽  
Air Temperature ◽  
Basal Metabolic Rate ◽  
Strong Relationship ◽  
The Other ◽  
Daily Torpor ◽  
Pronounced Increase ◽  
The Relationship

1. Rewarming rate from torpor and body mass were inversely related in 86 mammals ranging in body mass between 2 and 8500 g. 2. Most of the mammalian taxa investigated showed a similar change of rewarming rate with body mass. Only the insectivores showed a more pronounced increase in rewarming with a decrease in body mass than did the other taxa. The rates of rewarming of marsupials were similar to those of placentals. 3. At low air temperature (Ta), the rate of rewarming of marsupials was not related to body mass, although a strong relationship between the two variables was observed in the same species at high Ta. 4. The slopes relating rewarming rates and body mass of the mammalian groups and taxa analysed here were similar to those obtained earlier for mass-specific basal metabolic rate (BMR) and body mass in mammals, suggesting that the rate of rewarming and BMR are physiologically linked.

scholarly journals The relationship between genome size and metabolic rate in extant vertebrates

2020 ◽  
Vol 375 (1793) ◽  
pp. 20190146 ◽  
Author(s):  
Jacob D. Gardner ◽  
Michel Laurin ◽  
Chris L. Organ
Keyword(s):  
Metabolic Rate ◽  
Genome Size ◽  
Body Mass ◽  
Rate Variation ◽  
Large Nucleus ◽  
Functional Connection ◽  
Strong Positive Relationship ◽  
Elevated Rate ◽  
The Relationship ◽  
Rule Out

Genome size has long been hypothesized to affect the metabolic rate in various groups of animals. The mechanism behind this proposed association is the nucleotypic effect, in which large nucleus and cell sizes influence cellular metabolism through surface area-to-volume ratios. Here, we provide a review of the recent literature on the relationship between genome size and metabolic rate. We also conduct an analysis using phylogenetic comparative methods and a large sample of extant vertebrates. We find no evidence that the effect of genome size improves upon models in explaining metabolic rate variation. Not surprisingly, our results show a strong positive relationship between metabolic rate and body mass, as well as a substantial difference in metabolic rate between endothermic and ectothermic vertebrates, controlling for body mass. The presence of endothermy can also explain elevated rate shifts in metabolic rate whereas genome size cannot. We further find no evidence for a punctuated model of evolution for metabolic rate. Our results do not rule out the possibility that genome size affects cellular physiology in some tissues, but they are consistent with previous research suggesting little support for a direct functional connection between genome size and basal metabolic rate in extant vertebrates. This article is part of the theme issue ‘Vertebrate palaeophysiology’.

Sign in / Sign up

Export Citation Format

Share Document