Humidity-dependent colour change in the green forester moth, Adscita statices

Bodo D Wilts1, Karolina Mothander2, Almut Kelber2

  • 1Adolphe Merkle Institute, University of Fribourg, Chemin des Verdiers 4, 1700 Fribourg, Switzerland.

Biology Letters
|September 19, 2019
PubMed

Related Concept Videos

Electrophysiological Measurements from a Moth Olfactory System06:16

Electrophysiological Measurements from a Moth Olfactory System

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single sensillum recordings (SSR), which are summed responses from all odorant receptor neurons (ORNs) located on the antenna and from those housed in individual sensilla, respectively.
14.3K
Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils09:16

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring...
17.3K
Simulating Impacts of Ice Storms on Forest Ecosystems06:27

Simulating Impacts of Ice Storms on Forest Ecosystems

Ice storms are important weather events that are challenging to study because of difficulties in predicting their occurrence. Here, we describe a novel method for simulating ice storms that involves spraying water over a forest canopy during sub-freezing...
7.4K
Nanostructured Ag-zeolite Composites as Luminescence-based Humidity Sensors07:13

Nanostructured Ag-zeolite Composites as Luminescence-based Humidity Sensors

A protocol for the synthesis of moisture-responsive luminescent Ag-zeolite composites is described in this...
10.6K
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Recent advancements in remotely piloted aircraft systems (RPAS) allow sub-meter resolution, ideal for forest recovery monitoring. Integrating artificial intelligence (AI) enables deeper insights from large remotely sensed datasets. This protocol improves monitoring by supporting more efficient assessment and management of forested lands recovering from...
496