An Autonomous Lighted Insect Trap to Assess Aquatic Ecosystem Health

University of Wisconsin mechanical engineering students are developing and testing a digital insect light trap that will use low-cost ultraviolet LEDs, cameras, and micro-computers to assess insect populations cheaper, faster, and better than current methods. Dubbed the Stream Night Life, this device will be used to measure the health of streams and other aquatic and terrestrial ecosystems by monitoring the insects that are drawn to the traps.

The Need for Insect Population Data

Aquatic insects and other invertebrates are key links in both terrestrial and aquatic food webs and sensitive indicators of environmental health. Both angler observations by fly fishers and scientific studies indicate aquatic insect species diversity and populations are declining. While there’s debate among scientists regarding the magnitude and rate of these declines and what factors may be most responsible, there is little disagreement that the amount of aquatic and terrestrial insect population data currently available is woefully inadequate to document spatial or temporal insect population trends.

For example, the Wisconsin Department of Natural Resources collects about 400 aquatic invertebrate samples annually; that equates to about one sample for every 100 miles of streams in the state. These samples are processed at two state university labs, costing hundreds of dollars per sample and requiring 6–8 months for the resulting data. Only a few pest insect species, such as emerald ash borers and spongy moths, are tracked by state and federal agencies. Moreover, the U.S. EPA’s National Rivers and Streams Assessment collects aquatic insect taxonomic data on a national scale only every four years, and there are no systematic efforts to track adult aquatic insects. Better monitoring data are fundamental for improving our understanding of insect diversity and population trends in both aquatic and terrestrial environments.

Previous Insect Light Trapping Method

For over 100 years, lighted insect traps have been used to assess insect populations. Traps are often comprised of white bedsheets suspended by a rope and illuminated with gas lanterns or electric lamps. During the night, entomologists either pluck the specimens of interest off the sheet or the sheet is draped in a tub of an alcohol solution to trap the insects drawn to the light. After the night’s trapping, entomologists then sieve the drowned insects out of the tub and begin the laborious task of sorting and identifying the specimens collected.

New Technologies

With global concerns about declining wildlife populations, new camera trap technologies are being developed to improve the assessment of animal species worldwide. Like trail cams, camera traps collect imagery data, but with the addition of microcomputers and artificial intelligence, the images and associated environmental data can be processed automatically in the field to identify and count the animals of interest and identify key environmental attributes of the animals’ ecology. Some of these devices transmit sampling data collected at remote field locations to the researcher’s office computers via cellular links. The DIOPSIS project in the Netherlands and the AMI-trap in the UK have developed camera-based insect monitoring systems.

Light Trap Operation

At night, the autonomous Stream Night Life will power up and photograph insects attracted to a stage lit by ultraviolet LEDs. The imagery data will be captured on an onboard microcomputer and then transmitted to a computer server via a cellular link. A primary goal of the project is to ultimately use artificial intelligence to automatically identify and quantify the flying insects drawn to the traps.

A major task in this effort will be the collection of the hundreds of photographs necessary to develop the computer algorithms used to identify and count the insects. Images of individual adult aquatic insects will be identified, labeled, and fed into a Deep Learning program to train software to identify and count specimens. The resulting algorithm will then be used to identify and count the insects captured in subsequent photographs.

Seeking Collaboration

The plan is to design a low-cost, open-source, relatively easy-to-build device that can be deployed by a consortium of state and federal agencies, and private organizations like Trout Unlimited. Collaboration on crowdsourcing of the annotated images will help expedite the Deep Learning process. Initially, the insect taxonomic identifications will be relatively coarse, most likely at the Order level (for example, mayflies, caddisflies, stoneflies). Abundances and proportions of each of these insect taxa and how they differ over time and among locations can provide powerful information on the conditions of streams and other aquatic and terrestrial environments. Imagery data collected at stream sites will be compared against traditional aquatic kick net methods to assess how representative the light trap samples are of the entire assemblage of aquatic invertebrates found in streams.

The engineers are focused on advancing the device’s functionality and stability of the initial prototype built in 2023; a major structural redesign to improve field ruggedness and ease of deployment comes next. The device uses a Raspberry Pi to control the camera, lighting, data storage, and transmission. Additional refinements being considered include sensors for air temperature, humidity, and wind speed, given that these environmental factors strongly influence insect activity.

The students are working with stream scientists investigating which adult aquatic insect taxa are drawn to light traps, how representative these taxa are of entire stream invertebrate assemblages, and what biological indexes of stream health can be developed to interpret the trap data. A goal for this year is to build a more advanced prototype for summer field testing in 2025. The students receive guidance from University of Wisconsin Mechanical Engineering Professor Graham Wabiszewski and stream ecologist Mike Miller of the Wisconsin Department of Natural Resources.

Again, we welcome feedback (michaela.miller@wisconsin.gov) from those interested in collaboration in crowdsourcing of adult aquatic insect images, engineering feedback on the prototype design, or field testers of an advanced prototype in 2025.