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

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Group Design

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...

The Scientific Method

The Scientific Method

Research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

You might also read

Related Articles

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

Sort by

Same author

Ploidy level predicts differences in minimum leaf conductance in quaking aspen, Populus tremuloides.

American journal of botany·2026

Same author

A general framework for invasion cycles in ecology.

Ecology·2026

Same author

Wind-driven seed dispersal differentially promotes seed trapping and retention across alpine plants.

American journal of botany·2026

Same author

Reconciling links between diversity and population stability across global plant communities.

The New phytologist·2026

Same author

Plant-Pollinator Interaction Rewiring Boosts Year-to-Year Community Persistence.

Ecology letters·2026

Same author

Evolution in Response to an Abiotic Stress Shapes Species Coexistence.

Ecology letters·2025

Search research articles

Related Experiment Video

Updated: Jun 10, 2025

Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience

Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience

Published on: November 15, 2024

Predicting and Prioritising Community Assembly: Learning Outcomes via Experiments.

Benjamin W Blonder¹, Michael H Lim², Oscar Godoy³

¹Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, California, USA.

Ecology Letters

|October 12, 2024

Summary

This summary is machine-generated.

Predicting ecological community assembly is challenging. A new method, Learning Outcomes Via Experiments (LOVE), uses experimental data to accurately predict and prioritize community assembly outcomes for conservation and restoration.

Keywords:

coexistence community assembly community ecology ethics machine learning prediction prioritisation synthetic ecology

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Related Experiment Videos

Last Updated: Jun 10, 2025

Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience

Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience

Published on: November 15, 2024

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Ecological community assembly
Applied ecology
Biodiversity science

Background:

Predicting and prioritizing ecological community assembly outcomes is crucial for biodiversity conservation, climate change adaptation, invasion biology, restoration ecology, and synthetic ecology.
Current methods often rely on detailed mechanistic understanding, which may not be available or feasible in all scenarios.

Purpose of the Study:

To introduce and validate a mechanism-free approach, Learning Outcomes Via Experiments (LOVE), for predicting and prioritizing ecological community assembly.
To demonstrate the utility of LOVE across diverse ecological datasets and applied challenges.

Main Methods:

LOVE involves conducting ecological assembly experiments with various species addition combinations ('actions') across different environments.
Abundance outcomes are measured, and a predictive model is trained on experimental data.
The model is then used to predict outcomes of novel actions or prioritize actions for specific ecological goals.

Main Results:

Across 10 datasets, LOVE achieved a mean error of 0.5%-3.4% in predicting community assembly outcomes when trained on 89 random actions.
LOVE successfully prioritized actions for maximizing species richness, abundance, or removing undesirable species.
The method demonstrated high true positive rates (94%-99%) and variable true negative rates (10%-84%) across different prioritization tasks.

Conclusions:

LOVE offers a practical, data-driven approach to predicting and prioritizing ecological community assembly, particularly useful when mechanistic knowledge is limited.
This method complements existing mechanism-based approaches and has broad applicability to applied ecological challenges.
LOVE can aid in designing effective strategies for biodiversity conservation, ecological restoration, and managing invasive species.