ReviewEntropy (Basel, Switzerland)2018
Entropy, or Information, Unifies Ecology and Evolution and Beyond.
Review in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Safety assessment scheme for menstrual cups and application for the evaluation of a menstrual cup comprised of medical grade silicone.EBioMedicine · 2022Trial
- Farmed Escapees Threaten MHC Diversity in Wild Atlantic Salmon.Evolutionary applications · 2026Article
- Life as a Categorical Information-Handling System: An Evolutionary Information-Theoretic Model of the Holobiont.Biology · 2026Review
- Principles of Molecular Evolution: Concepts from Non-equilibrium Thermodynamics for the Multilevel Theory of Learning.Journal of molecular evolution · 2024Article
- Article
- Potential Benefits and Challenges of Quantifying Pseudoreplication in Genomic Data with Entropy Statistics.Entropy (Basel, Switzerland) · 2024Article
- Review
- Predicting the spread of SARS-CoV-2 variants: An artificial intelligence enabled early detection.PNAS nexus · 2024Article
- Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems.Entropy (Basel, Switzerland) · 2023Article
- The safety assessment of tampons: illustration of a comprehensive approach for four different products.Frontiers in reproductive health · 2023Article
- Bray-Curtis (AFD) differentiation in molecular ecology: Forecasting, an adjustment (Ecology and evolution · 2022Article
- Article
- The Introduction of Entropy and Information Methods to Ecology by Ramon Margalef.Entropy (Basel, Switzerland) · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article discusses how entropy/information methods are well-suited to analyzing and forecasting the four processes of innovation, transmission, movement, and adaptation, which are the common basis to ecology and evolution. Macroecologists study assemblages of differing species, whereas micro-evolutionary biologists study variants of heritable information within species, such as DNA and epigenetic modifications. These two different modes of variation are both driven by the same four basic processes, but approaches to these processes sometimes differ considerably. For example, macroecology often documents patterns without modeling underlying processes, with some notable exceptions. On the other hand, evolutionary biologists have a long history of deriving and testing mathematical genetic forecasts, previously focusing on entropies such as heterozygosity. Macroecology calls this Gini-Simpson, and has borrowed the genetic predictions, but sometimes this measure has shortcomings. Therefore it is important to note that predictive equations have now been derived for molecular diversity based on Shannon entropy and mutual information. As a result, we can now forecast all major types of entropy/information, creating a general predictive approach for the four basic processes in ecology and evolution. Additionally, the use of these methods will allow seamless integration with other studies such as the physical environment, and may even extend to assisting with evolutionary algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.