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

Adaptations that Reduce Water Loss

Adaptations that Reduce Water Loss

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.

Aqueous Solutions and Heats of Hydration

Aqueous Solutions and Heats of Hydration

Water and other polar molecules are attracted to ions. The electrostatic attraction between an ion and a molecule with a dipole is called an ion-dipole attraction. These attractions play an important role in the dissolution of ionic compounds in water.
When ionic compounds dissolve in water, the ions in the solid separate and disperse uniformly throughout the solution because water molecules surround and solvate the ions, reducing the strong electrostatic forces between them. This process...

Hydration of Cement

Hydration of Cement

Hydration of cement is a chemical reaction between cement particles and water. This process occurs primarily through two mechanisms: through-solution and topochemical. In the through-solution process, anhydrous compounds dissolve into their constituents, hydrates form in the solution, and then precipitate from the supersaturated solution. The topochemical process involves solid-state reactions at the cement particle surface. The through-solution process dominates the topochemical process at the...

Model Approaches for Pharmacokinetic Data: Physiological Models

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

You might also read

Related Articles

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

Sort by

Same author

Mapping cumulative pressures on the grazing lands of northern Fennoscandia.

Scientific reports·2022

Same author

Multiple drivers of large-scale lichen decline in boreal forest canopies.

Global change biology·2022

Same author

Tansley Review No. 117: Carbon economy in lichens.

The New phytologist·2021

Same author

Carbonic anhydrase(s) associated with lichens: in vivo activities, possible locations and putative roles.

The New phytologist·2021

Same author

Anthropogenic nitrogen enrichment enhances soil carbon accumulation by impacting saprotrophs rather than ectomycorrhizal fungal activity.

Global change biology·2019

Same author

Levels of forest ecosystem services depend on specific mixtures of commercial tree species.

Nature plants·2019

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

Published on: March 25, 2019

Predicting lichen hydration using biophysical models.

Anna V Jonsson¹, Jon Moen, Kristin Palmqvist

¹Department of Ecology and Environmental Science, Umeå University, 901 87 Umeå, Sweden. anna.jonsson@emg.umu.se

|February 29, 2008

Summary

This summary is machine-generated.

Two models accurately predict lichen hydration status, crucial for understanding lichen productivity. These models, one biophysical and one physical, offer insights into environmental factors influencing lichen wet periods.

More Related Videos

Methods for the Self-integration of Megamolecular Biopolymers on the Drying Air-LC Interface

Methods for the Self-integration of Megamolecular Biopolymers on the Drying Air-LC Interface

Published on: April 7, 2017

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Published on: September 11, 2016

Related Experiment Videos

Last Updated: Jul 7, 2026

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

Published on: March 25, 2019

Methods for the Self-integration of Megamolecular Biopolymers on the Drying Air-LC Interface

Methods for the Self-integration of Megamolecular Biopolymers on the Drying Air-LC Interface

Published on: April 7, 2017

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Published on: September 11, 2016

Area of Science:

Ecology
Biophysics
Physiology

Background:

Lichen productivity is influenced by hydration dynamics.
Predicting lichen hydration status is key for mechanistic productivity models.

Purpose of the Study:

Develop and validate models for predicting lichen hydration status.
Assess the accuracy of biophysical and physical models for different lichen species.

Main Methods:

Developed a biophysical model using air water potential, temperature, humidity, and species-specific constants.
Developed a reduced physical model assuming instantaneous lichen-air equilibration.
Validated models using field and lab data for Platismatia glauca, Alectoria sarmentosa, and Cladina rangiferina.

Main Results:

Both models accurately predicted hydration period length and timing for Alectoria sarmentosa and Platismatia glauca.
Model accuracy was lower for Cladina rangiferina compared to epiphytic species.
The biophysical model successfully simulated stochastic lichen hydration from environmental data.

Conclusions:

Lichen hydration status can be simulated using biophysical data.
Developed models provide a foundation for assessing lichen productivity under varying microclimates.
Future work should incorporate light and temperature during hydration for enhanced productivity predictions.