Automatic traps foster the monitoring and prediction of bark beetle swarming

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.