Back

Plant-derived CO2 mediates long-distance host location and quality assessment by a root herbivore

Theepan, V.; Schimmel, B. C. J.; Arce, C. C. M.; Erb, M.; Machado, R. A. R.

2020-03-12 ecology
10.1101/2020.03.12.988691 bioRxiv
Show abstract

Insect herbivores can use volatile and visual cues to locate and select suitable host plants from a distance. The importance of CO2, arguable the most conserved volatile marker of metabolic activity, is not well understood in this context, even though many herbivores are known to respond to minute differences in CO2 concentrations. To address this gap of knowledge, we manipulated CO2 perception of the larvae of the western corn rootworm (Diabrotica virgifera virgifera; WCR) through RNA interference and studied how CO2 perception impacts their interaction with their host plant, maize (Zea mays). We show that the expression of a putative Group 2 carbon dioxide receptor, DvvGr2, is specifically required for dose-dependent larval responses to CO2 in the ppm range. Silencing DvvGr2 has no effect on the ability of WCR larvae to locate host plants at short distance (<9 cm), but impairs host location at greater distances. Using soil arenas and olfactometer experiments in combination with DvvGr2 silencing and CO2 scrubbing, we demonstrate that WCR larvae use CO2 as a long-range host plant finding cue, but employ other volatiles for short-range host location. We furthermore show that the larvae use CO2 as a fitness-relevant long-distance indicator of plant nutritional status: Maize plants that are well-fertilized emit more CO2 from their roots and are better hosts for WCR than plants that are nutrient-deficient, and the capacity of WCR larvae to distinguish between these plants depends exclusively on their capacity to perceive CO2 through DvvGr2. This study unravels how CO2 can mediate plant-herbivore interactions by serving as a distance-dependent host location and quality assessment cue.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.