Skip to main content
Dryad

Automatic traps foster the monitoring and prediction of bark beetle swarming

Hofmann, Sven1; Kautz, Markus1

Published Mar 27, 2026 on Dryad. https://doi.org/10.5061/dryad.pvmcvdp17

Data files

Mar 27, 2026 version files 17.45 MB

Click names to download individual files

Abstract

The abundance of forest pest insects such as bark beetles is commonly assessed using pheromone traps emptied at regular intervals. While providing a rough estimate of infestation risk, trap-based monitoring programs come with multiple drawbacks, e.g. notable costs as well as limitations regarding the resolution, timeliness and area-wide availability of data. Swarming models can overcome these drawbacks – however, such models rely on accurate swarming data.

To this aim, we developed a novel automatic pheromone trapping system and applied it to assess swarming intensity of three tree-killing bark beetle species, i.e. Ips typographus, Pityogenes chalcographus, and Pityokteines curvidens. The recorded data on swarming and related climatic parameters, covering multiple sites and years along an elevation gradient in Southwest Germany, were subsequently used to calibrate predictive swarming models.

Temperature was identified as the most significant driver of swarming intensity across all three bark beetle species, showing a strong alignment with established developmental rate curves. Additional factors influencing swarming patterns included global radiation, day of year, study site, and pheromone release.

The automatic traps deliver highly accurate real-time data, enabling timely and area-wide predictions, which can be directly integrated into digital risk assessment tools.

Synthesis and applications: Automated trap data help bark beetle management to act more timely and targeted, thereby facilitating an effective mitigation of outbreaks. Moreover, the immediate data transmission makes regular manual trap collections from the traps unnecessary. While swarming models cannot quantify absolute trap catches without site- and trap-specific calibration, they provide robust predictions for relative swarming intensity at the stand scale. Integrated into dynamic risk models, they can be seen as the next step towards a digitalization of pest monitoring, and are likely to complement or even replace conventional monitoring programs in the future.