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 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 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 Resting metabolic rate and morphology in mice (Mus musculus) selected for high and low food intake

2001 ◽  
Vol 204 (4) ◽  
pp. 777-784 ◽  
Author(s):  
C. Selman ◽  
S. Lumsden ◽  
L. Bunger ◽  
W.G. Hill ◽  
J.R. Speakman
Keyword(s):  
Regression Analysis ◽  
Food Intake ◽  
Metabolic Rate ◽  
Body Mass ◽  
Resting Metabolic Rate ◽  
Strain Differences ◽  
Dry Mass ◽  
Intestine Length ◽  
Body Components ◽  
The Relationship

We investigated the relationship between resting metabolic rate (RMR) and various morphological parameters in non-breeding mice, selected for high and low food intake corrected for body mass. RMR was measured at 30 degrees C, and mice were subsequently killed and dissected into 19 body components. High-food-intake mice had significantly greater body masses and a significantly elevated RMR compared with the low-intake mice. Data pooled across strains indicated that body mass, sex and strain together explained over 56 % of the observed variation in RMR. The effects of strain and sex on RMR and tissue morphology were removed, and three separate statistical analyses to investigate the relationship between RMR and organ morphology were performed: (i) employing individual regression analysis with each tissue component as a separate predictor against RMR; (ii) individual regression analysis with residual organ mass against residual RMR (i.e. with strain, sex and body mass effects removed); and (iii) pooling of some organ masses into functional groupings to reduce the number of predictors. Liver mass was the most significant morphological trait linked to differences in RMR. Small intestine length was significantly greater in the high-intake line; however, no difference was observed between strains in the dry mass of this organ, and there was no evidence to associate variability in the mass of the alimentary tract with variability in RMR. The effects of strain on RMR independent of the effect on body mass were consistent with the anticipated effect from the strain differences in the size of the liver.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

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.

Sign in / Sign up

Export Citation Format

Share Document