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

Ecological Niches02:02

Ecological Niches

27.4K
All organisms have a position within an ecosystem. The complete set of living and nonliving factors—including food resources, climate, and terrain—that define the position of a given organism are collectively referred to as the organism’s ecological niche.
27.4K
Optimal Foraging00:48

Optimal Foraging

14.2K
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.
14.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

407
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
407
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

335
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...
335
What are Populations and Communities?00:30

What are Populations and Communities?

38.6K
Overview
38.6K
Conservation of Small Populations02:04

Conservation of Small Populations

17.7K
Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
17.7K

You might also read

Related Articles

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

Sort by
Same author

Accelerating reaction kinetics of AlCl<sub>3</sub>/acetamide electrolyte by co-solvation for Al-S batteries.

Materials horizons·2026
Same author

Regular Aerobic Exercise Can Effectively Ameliorate the Skeletal Muscle and Mitochondrial Function Impairments Caused by <i>bves</i> Deficiency in Zebrafish.

International journal of molecular sciences·2026
Same author

Grass carp DDX3X inhibits SVCV replication by activating IFN through the TRAF6-IRF3/7 signaling axis.

Developmental and comparative immunology·2026
Same author

Imaging of a van der Waals spin-orbit torque system using spin ensembles in hBN.

Nature communications·2026
Same author

STOPOVER HOTSPOTS FOR MIGRATORY BIRDS IN NORTH AND CENTRAL AMERICA.

Biodiversity informatics·2026
Same author

A machine learning-driven framework integrating cell death and senescence signatures for multi-target drug design and immunotherapy optimization in ovarian cancer.

NPJ precision oncology·2026

Related Experiment Video

Updated: Apr 3, 2026

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

8.1K

Marble Algorithm: a solution to estimating ecological niches from presence-only records.

Huijie Qiao1, Congtian Lin1, Zhigang Jiang1

  • 1Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.

Scientific Reports
|September 22, 2015
PubMed
Summary

The Marble Algorithm (MA) predicts species distribution using ecological niche modeling. This density-based clustering method accurately identifies potential habitats, especially with limited occurrence data.

More Related Videos

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes
09:10

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes

Published on: March 1, 2022

3.0K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K

Related Experiment Videos

Last Updated: Apr 3, 2026

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

8.1K
A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes
09:10

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes

Published on: March 1, 2022

3.0K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.9K

Area of Science:

  • Ecology
  • Biodiversity research
  • Computational biology

Background:

  • Ecological niches are complex multidimensional hypervolumes in environmental space.
  • Predicting species distribution is crucial for conservation and understanding biodiversity.
  • Existing distribution modeling tools have varying performance and transferability.

Purpose of the Study:

  • To introduce and evaluate the Marble Algorithm (MA) for predicting potential species distributional areas.
  • To compare MA's performance against established distribution-modeling tools.
  • To assess MA's accuracy, robustness, and transferability, particularly with sparse occurrence data.

Main Methods:

  • The Marble Algorithm (MA) employs density-based spatial clustering of applications with noise (DBSCAN) to characterize ecological niche space.
  • MA groups occurrence points in environmental space into clusters representing the ecological niche.
  • MA was tested using virtual species and ten empirical datasets, comparing it with 13 other distribution-modeling tools.

Main Results:

  • MA demonstrated high accuracy in predicting potential distributional areas across all tested datasets.
  • The algorithm showed moderate robustness and above-average transferability compared to other methods.
  • MA performed particularly well when analyzing datasets with a small number of species occurrences.

Conclusions:

  • The Marble Algorithm is an effective tool for ecological niche modeling and predicting species distributions.
  • MA offers a valuable alternative for distribution modeling, especially in data-limited scenarios.
  • The algorithm's performance highlights the utility of density-based clustering for ecological applications.