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 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 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 On the comparative biology of mammalian telomeres: Telomere length co-evolves with body mass and lifespan

2020 ◽  
Author(s):  
Michael Le Pepke ◽  
Dan Eisenberg
Keyword(s):  
Telomere Length ◽  
Body Mass ◽  
Dna Sequences ◽  
Replicative Senescence ◽  
Telomerase Activity ◽  
Sensitivity Analyses ◽  
Inverse Association ◽  
Mammal Species ◽  
Data Set ◽  
Telomere Biology

Telomeres, the short repetitive DNA sequences that cap the ends of linear chromosomes, shorten during cell division and are implicated in senescence in most species. The enzyme telomerase can rebuild telomeres but is repressed in many mammals that exhibit replicative senescence, presumably as a tumor suppression mechanism. It is therefore important that we have an accurate understanding of the (co-)evolution of telomere biology and life-history traits that has shaped the diversity of senescence patterns across species. Gomes et al. (2011) produced a large data set on telomere length (TL), telomerase activity, body mass and lifespan among 57 mammal species. We re-analyzed their data set using the same phylogenetic multiple regressions presented in the original publication and several sensitivity analyses. We found substantial inconsistencies in our results compared to Gomes et al.'s. Consistent with Gomes et al. we found an inverse association between TL and lifespan. Contrary to the analyses reported in Gomes et al., we found that there was a generally robust inverse association between TL and mass, and only weak non-robust evidence for an association between telomerase activity and mass. These results suggest that shorter TL may have been selected for in larger and longer-lived species – likely as a mechanism to suppress cancer. However, our results call into question past results suggesting that high telomerase activity has been selected against in larger species and stress the need for careful attention to model construction and sensitivity analyses.

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.

Visualization of Cartilage from Knee Joint Magnetic Resonance Images and Quantitative Assessment to Study the Effect of Age, Gender and Body Mass Index (BMI) in Progressive Osteoarthritis (OA)

2019 ◽  
Vol 15 (6) ◽  
pp. 565-572
Author(s):  
Mallikarjunaswamy Shivagangadharaiah Matada ◽  
Mallikarjun Sayabanna Holi ◽  
Rajesh Raman ◽  
Sujana Theja Jayaramu Suvarna
Keyword(s):  
Body Mass Index ◽  
Magnetic Resonance ◽  
Knee Joint ◽  
Body Mass ◽  
3D Visualization ◽  
Cartilage Degradation ◽  
Magnetic Resonance Images ◽  
Cartilage Thickness ◽  
Knee Joints ◽  
Data Set

Background: Osteoarthritis (OA) is a degenerative disease of joint cartilage affecting the elderly people around the world. Visualization and quantification of cartilage is very much essential for the assessment of OA and rehabilitation of the affected people. Magnetic Resonance Imaging (MRI) is the most widely used imaging modality in the treatment of knee joint diseases. But there are many challenges in proper visualization and quantification of articular cartilage using MRI. Volume rendering and 3D visualization can provide an overview of anatomy and disease condition of knee joint. In this work, cartilage is segmented from knee joint MRI, visualized in 3D using Volume of Interest (VOI) approach. Methods: Visualization of cartilage helps in the assessment of cartilage degradation in diseased knee joints. Cartilage thickness and volume were quantified using image processing techniques in OA affected knee joints. Statistical analysis is carried out on processed data set consisting of 110 of knee joints which include male (56) and female (54) of normal (22) and different stages of OA (88). The differences in thickness and volume of cartilage were observed in cartilage in groups based on age, gender and BMI in normal and progressive OA knee joints. Results: The results show that size and volume of cartilage are found to be significantly low in OA as compared to normal knee joints. The cartilage thickness and volume is significantly low for people with age 50 years and above and Body Mass Index (BMI) equal and greater than 25. Cartilage volume correlates with the progression of the disease and can be used for the evaluation of the response to therapies. Conclusion: The developed methods can be used as helping tool in the assessment of cartilage degradation in OA affected knee joint patients and treatment planning.

scholarly journals Allometry of mitochondrial efficiency is set by metabolic intensity

2019 ◽  
Vol 286 (1911) ◽  
pp. 20191693 ◽  
Author(s):  
Boël Mélanie ◽  
Romestaing Caroline ◽  
Voituron Yann ◽  
Roussel Damien
Keyword(s):  
Metabolic Rate ◽  
Body Mass ◽  
Coupling Efficiency ◽  
Energy Output ◽  
Scaling Exponent ◽  
Metabolic Rates ◽  
Mammal Species ◽  
Skeletal Muscle Mitochondria ◽  
Metabolic Intensity ◽  
Mitochondrial Efficiency

Metabolic activity sets the rates of individual resource uptake from the environment and resource allocations. For this reason, the relationship with body size has been heavily documented from ecosystems to cells. Until now, most of the studies used the fluxes of oxygen as a proxy of energy output without knowledge of the efficiency of biological systems to convert oxygen into ATP. The aim of this study was to examine the allometry of coupling efficiency (ATP/O) of skeletal muscle mitochondria isolated from 12 mammal species ranging from 6 g to 550 kg. Mitochondrial efficiencies were measured at different steady states of phosphorylation. The efficiencies increased sharply at higher metabolic rates. We have shown that body mass dependence of mitochondrial efficiency depends on metabolic intensity in skeletal muscles of mammals. Mitochondrial efficiency positively depends on body mass when mitochondria are close to the basal metabolic rate; however, the efficiency is independent of body mass at the maximum metabolic rate. As a result, it follows that large mammals exhibit a faster dynamic increase in ATP/O than small species when mitochondria shift from basal to maximal activities. Finally, the invariant value of maximal coupling efficiency across mammal species could partly explain why scaling exponent values are very close to 1 at maximal metabolic rates.

scholarly journals Gizzard vs. teeth, it's a tie: food-processing efficiency in herbivorous birds and mammals and implications for dinosaur feeding strategies

Paleobiology ◽  
2011 ◽  
Vol 37 (4) ◽  
pp. 577-586 ◽  
Author(s):  
Julia Fritz ◽  
Jürgen Hummel ◽  
Ellen Kienzle ◽  
Oliver Wings ◽  
W. Jürgen Streich ◽  
...  
Keyword(s):  
Particle Size ◽  
Food Processing ◽  
Bird Species ◽  
Size Reduction ◽  
Feeding Strategies ◽  
Mammal Species ◽  
Particle Size Reduction ◽  
Processing Efficiency ◽  
Data Set ◽  
Significant Difference

Particle size reduction is a primary means of improving efficiency in herbivores. The mode of food particle size reduction is one of the main differences between herbivorous birds (gizzard) and mammals (teeth). For a quantitative comparison of the efficiency of food comminution, we investigated mean fecal particle sizes (MPS) in 14 herbivorous bird species and compared these with a data set of 111 non-ruminant herbivorous mammal species. In general MPS increased with body mass, but there was no significant difference between birds and mammals, suggesting a comparable efficiency of food processing by gizzards and chewing teeth. The results lead to the intriguing question of why gizzard systems have evolved comparatively rarely among amniote herbivores. Advantages linked to one of the two food comminution systems must, however, be sought in different effects other than size reduction itself. In paleoecological scenarios, the evolution of “dental batteries,” for example in ornithopod dinosaurs, should be considered an advantage compared to absence of mastication, but not compared to gizzard-based herbivory.

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