Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Soil Ecosystem

The Soil Ecosystem

Plants obtain inorganic minerals and water from the soil, which acts as a natural medium for land plants. The composition and quality of soil depend not only on the chemical constituents but also on the presence of living organisms. In general, soils contain three major components:

Ecological Succession

Ecological Succession

Ecological succession is influenced by the processes of facilitation, inhibition, and toleration. Facilitation occurs when early successional species create more favorable ecological conditions for subsequent species, such as enhanced nutrient, water, or light availability. In contrast, inhibition happens when early successional species create unfavorable ecological conditions for potential successive species, such as limiting resource availability. In some cases, later successional species...

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

Trophic Efficiency

Trophic Efficiency

Trophic level transfer efficiency (TLTE) is a measure of the total energy transfer from one trophic level to the next. Due to extensive energy loss as metabolic heat, an average of only 10% of the original energy obtained is passed on to the next level. This pattern of energy loss severely limits the possible number of trophic levels in a food chain.

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...

You might also read

Related Articles

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

Sort by

Same author

Modelling belowground plant acclimation to low soil nitrogen - a heuristic optimality-based approach.

The New phytologist·2026

Same author

Microbial growth rate is a stronger predictor of soil organic carbon than carbon use efficiency.

Nature ecology & evolution·2026

Same author

Microbiome Adaptation Could Amplify Modeled Projections of Global Soil Carbon Loss With Climate Warming.

Global change biology·2025

Same author

A trait spectrum linking nitrogen acquisition and carbon use of ectomycorrhizal fungi.

The New phytologist·2025

Same author

Microbial Carbon Use Efficiency and Growth Rates in Soil: Global Patterns and Drivers.

Global change biology·2025

Same author

The Impact of Microbial Interactions on Ecosystem Function Intensifies Under Stress.

Ecology letters·2024

Same journal

Lucky To Be Alive, Luckier to Breed: Lifetime Reproduction in Weddell Seals.

Ecology letters·2026

Same journal

Three-Dimensional Correlated Random Walks for Animal Movement and Habitat Selection.

Ecology letters·2026

Same journal

Higher-Order Interactions Can Promote Coexistence by Rewiring Intransitivities Into Competitive Networks.

Ecology letters·2026

Same journal

Plants That Evolved Under High Phylogenetic Diversity Have Higher Invasion Success, Particularly in Undisturbed Communities.

Ecology letters·2026

Same journal

Predictors of Food Web Resistance to Environmental Change.

Ecology letters·2026

Same journal

AI, Comparative Advantage, and the Next Decade of Ecological Research.

Ecology letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 8, 2026

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

Published on: February 15, 2019

Eco-Evolutionary Optimality in Soil Organic Matter Models.

Erik Schwarz^1,2, Elsa Abs³, Arjun Chakrawal⁴

¹Department of Physical Geography, Stockholm University, Stockholm, Sweden.

Ecology Letters

|December 20, 2025

Summary

This summary is machine-generated.

Eco-evolutionary optimization (EEO) models soil microbial adaptation to environmental conditions, improving soil organic matter (SOM) models. Further research is needed to address challenges in EEO application and validation for SOM dynamics.

Keywords:

eco‐evolution mathematical models model development optimality theory soil carbon cycling soil microorganisms soil organic matter

More Related Videos

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

Published on: March 21, 2025

Related Experiment Videos

Last Updated: Jan 8, 2026

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

Published on: February 15, 2019

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

Published on: March 21, 2025

Area of Science:

Soil science
Microbial ecology
Biogeochemistry

Background:

Soil microorganisms drive essential carbon and nutrient cycles.
Microbial adaptation to environmental changes impacts soil organic matter (SOM) dynamics.
Current SOM models often overlook these eco-evolutionary dynamics.

Purpose of the Study:

To review and categorize Eco-Evolutionary Optimization (EEO) approaches for SOM models.
To provide a primer on EEO principles and applications.
To identify challenges and future research directions for EEO in SOM modeling.

Main Methods:

Literature synthesis of EEO approaches applied to SOM models.
Categorization of EEO approaches based on underlying assumptions.
Review of fitness proxies used in EEO.

Main Results:

EEO offers a framework to integrate microbial adaptation into SOM models.
Different EEO approaches exist, utilizing various microbial fitness proxies.
Challenges include implicit assumptions, prediction convergence, and empirical validation.

Conclusions:

EEO can enhance the realism and generality of SOM models.
Explicitly defining assumptions and validating predictions are crucial for EEO advancement.
Future research should focus on addressing current limitations for broader EEO application.