Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Entropy01:18

Entropy

The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be put...
The Entropy as a State Function01:14

The Entropy as a State Function

Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
Absolute Entropies and the Third Law of Thermodynamics01:23

Absolute Entropies and the Third Law of Thermodynamics

Ludwig Edward Boltzmann developed a definition for entropy, which stated that absolute entropy is proportional to the natural logarithm of the number of possible combinations of particles. Entropy stands alone among state functions as the only one whose absolute values can be determined.Consider a gas sample confined to a container. As the container expands, the energy levels of gas molecules become more closely spaced. This increases the number of available energy states, thereby increasing...
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Broadening an understanding of learners who think differently in medical education.

Medical teacher·2026
Same author

Prevalence and patterns of comorbid mental disorders in a male forensic psychiatric sample: A network analysis.

Psychiatry research·2025
Same author

The impact of low-mode symmetry on inertial fusion energy output in the burning plasma state.

Nature communications·2024
Same author

Hohlraum Reheating from Burning NIF Implosions.

Physical review letters·2024
Same author

Case of Abscess of the Left Lung; Thickening and Partial Ulceration of the Pericardium; Adhesion of the Right Lung, and Extensive Tubercular Deposition through the Entire Pulmonary Structure; with Obscure Diagnosis.

Medical examiner (Philadelphia, Pa.)·2023
Same author

Time trade-off study to establish utility decrements in individuals with a spinal cord injury who perform intermittent catheterization.

Journal of medical economics·2023
Same journal

Fuzzy relationships among plant dispersal mechanisms, syndromes, and animal vectors.

Ecology·2026
Same journal

Consequences of phenological shifts are determined by the number of generations per season.

Ecology·2026
Same journal

Mechanistic and scale-specific analyses advance the preference-performance hypothesis.

Ecology·2026
Same journal

Ground-to-canopy monitoring reveals hidden ecological patterns in Congo Basin mammals.

Ecology·2026
Same journal

Combining individual and close-kin mark-recapture to design an effective wildlife population survey.

Ecology·2026
Same journal

Cross-stressor resilience of soil microbial growth and carbon metabolism under climate change.

Ecology·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Maximum entropy and the state-variable approach to macroecology.

J Harte1, T Zillio, E Conlisk

  • 1Energy and Resources Group, University of California, 310 Barrows Hall, Berkeley, California 94720, USA. jharte@berkeley.edu

Ecology
|October 31, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new theoretical framework using information entropy to predict biodiversity scaling metrics, like species-area relationships and abundance distributions, based on ecosystem state variables.

More Related Videos

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Related Experiment Videos

Last Updated: Jun 28, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Area of Science:

  • Macroecology
  • Theoretical Ecology
  • Statistical Physics

Background:

  • Macroecology studies biodiversity scaling metrics such as species-area relationships (SAR) and species-abundance distributions (SAD).
  • Existing theories often rely on specific ecological mechanisms to explain these patterns.
  • A unified theoretical framework is needed to predict diverse macroecological metrics.

Purpose of the Study:

  • To develop a theoretical framework for predicting biodiversity scaling metrics.
  • To utilize the state-variable concept and information theory for macroecological predictions.
  • To explain established ecological scaling laws from first principles.

Main Methods:

  • Proposed a theoretical framework based on ecosystem state variables (area, species count, individual count, metabolic energy rate).
  • Applied an analytical method derived from information entropy, analogous to statistical physics.
  • Derived scaling forms for SAR, SAD, abundance-mass relationships, and occupancy distributions.

Main Results:

  • Inferred realistic functional forms for macroecological metrics using information entropy and ratios of state variables.
  • The Fisher log series SAD emerged naturally from the theory.
  • Predicted SAR with negative log-log curvature, approximating a power law (z=0.14-0.20) at high species-to-individual ratios.
  • Predicted Damuth's scaling rule relating mass and abundance using the 3/4 power mass-metabolism relation.

Conclusions:

  • The theoretical framework successfully predicts key macroecological metrics without adjustable parameters.
  • Predicted patterns show reasonable agreement with empirical data from plant census studies.
  • Discrepancies between predictions and data can guide the identification of underlying ecological mechanisms.