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

Light Acquisition02:16

Light Acquisition

8.0K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.0K
Introduction to Plant Diversity02:22

Introduction to Plant Diversity

39.8K
From Water to Land
39.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
333
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

347
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
347
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

545
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
545
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

705
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
705

You might also read

Related Articles

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

Sort by
Same author

Leveraging remote sensing and crowd-sourced biodiversity data for enhanced plant functional trait mapping.

Nature communications·2026
Same author

Quantifying the relative importance of disease-suppressive mechanisms in species mixtures: a case study of late blight in strip-intercropped potato.

Journal of experimental botany·2026
Same author

Plant traits explain variation in symbiotic nitrogen fixation responses to global nitrogen enrichment: a meta-analysis.

Nature communications·2026
Same author

Navigating the unknown: Nature-based solutions for coastal climate adaptation under deep uncertainty.

Journal of environmental management·2026
Same author

Unlocking Demography: Developing an eDNA-Based Toolkit to Measure Sex Ratios From Populations.

Molecular ecology resources·2025
Same author

Deconstructing host quality offers insight into disease ecology.

Trends in ecology & evolution·2025

Related Experiment Video

Updated: Apr 23, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.0K

A fully traits-based approach to modeling global vegetation distribution.

Peter M van Bodegom1, Jacob C Douma2, Lieneke M Verheijen2

  • 1Department of Ecological Science, Section of Systems Ecology, VU University Amsterdam, 1081 HV, Amsterdam, The Netherlands p.m.van.bodegom@vu.nl.

Proceedings of the National Academy of Sciences of the United States of America
|September 17, 2014
PubMed
Summary

A new study reveals that plant traits like leaf mass, stem density, and seed mass significantly predict global vegetation distribution. This trait-based approach accurately models vegetation patterns, advancing Dynamic Global Vegetation Models (DGVMs).

Keywords:
functional variationglobal vegetation mapprobabilistic modeltrait-environment relationshipsvegetation attributes

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.2K
JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning
09:23

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

Published on: March 21, 2025

1.8K

Related Experiment Videos

Last Updated: Apr 23, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.0K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.2K
JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning
09:23

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

Published on: March 21, 2025

1.8K

Area of Science:

  • Ecology
  • Global Change Biology
  • Biogeography

Background:

  • Dynamic Global Vegetation Models (DGVMs) are crucial for understanding climate change impacts on vegetation.
  • Integrating plant traits into DGVMs is an evolving area, but direct impacts on global distribution are not fully analyzed.
  • Existing DGVMs lack a comprehensive, trait-variation-driven approach to predict global vegetation patterns.

Purpose of the Study:

  • To conduct a comprehensive analysis of the direct impacts of plant trait variation on global vegetation distribution.
  • To develop and validate a fully traits-based approach for predicting vegetation patterns.
  • To demonstrate the potential of key plant traits in improving DGVM predictive capabilities.

Main Methods:

  • Regressed global trait observations (leaf mass per area, stem-specific density, seed mass) against environmental drivers.
  • Generated global trait maps by integrating regression equations with gridded climate and soil data.
  • Characterized nine vegetation types using trait combinations and Gaussian mixture density functions to predict occurrence probabilities.

Main Results:

  • Regression analysis explained up to 52% of global trait variation, with trait maps showing orders of magnitude variation.
  • The traits-based vegetation map correctly predicted 42% of observed vegetation distribution.
  • Leaf mass per area, stem-specific density, and seed mass were identified as key traits driving vegetation distribution.

Conclusions:

  • A significant portion of DGVM predictive power for vegetation distribution can be achieved using just three key traits.
  • The proposed traits-based approach and observation-driven trait maps offer a new framework for DGVM development.
  • This study paves the way for a new generation of powerful, traits-based DGVMs for climate change impact assessments.