The Biota Trace Platform
Biological signals are dynamic, often changing rapidly during rainfall, run-off and discharge events. Sampling should be too.
Short-lived peaks can be easy to miss with fixed schedules. The Biota Trace Platform connects sampling to changing environmental conditions, allowing samples to be taken when the most informative events for downstream analysis are happening.
The problem
The difference is not necessarily more sampling. It is using your existing sample allocation more effectively.
Molecular analysis has become more capable and accessible. Sensors and connectivity are now practical to deploy at scale. Scrutiny of water quality has risen sharply. Sampling, however, has changed far less. It is still often manual and calendar-driven, limiting what downstream analysis can reveal.
Scheduled sampling is valuable for establishing a baseline. Event-based sampling adds the opportunity to capture the conditions, transitions and peaks that help explain why that baseline changes.
The same signal below, sampled two ways: first on a fixed weekly schedule, then using the same sample allocation across scheduled and event-driven sampling to better capture and analyse the peak.
What changes
Each addresses a different limitation in conventional sampling. A systematic sample taken at the wrong moment can still miss an event. An event-driven sample without context can be difficult to interpret.
Use the same capture and handling approach each time, whether sampling is carried out in the laboratory or at the water's edge. That consistency helps distinguish genuine environmental change from variation introduced by the sampling process itself.
Trigger sampling in response to what is actually happening, such as a change in water quality, rainfall, run-off, an external data source or a remote instruction, rather than relying only on a fixed schedule. This makes it possible to capture short-lived events that scheduled sampling can miss.
Record environmental conditions alongside the sample rather than reconstructing them afterwards. Rainfall, flow, water quality and other contextual data can then be considered alongside the molecular result, helping explain what was happening at the point of capture.
The mechanism
A sampler on its own can only follow the instructions it was given before deployment. Once connected, it can respond to a nearby sensor, an external data source, a remote system or a user instruction.
How it connects
The gateway closes that gap. It carries telemetry over the network available at the site and relays instructions back to the instrument, allowing a sampler to respond to a reading taken somewhere else.
Which gateway you use depends on how many instruments, sensors and data sources need to feed the decision.
Connects a single instrument to the wider system, sending telemetry out and receiving rules and instructions back.
On-device start/stop, mode and power controls, together with status lights, allow the deployment to be started, checked and stopped at the water's edge without needing a phone or network connection.
Connects multiple instruments and sensors at one site, allowing rules to draw on several inputs before triggering an action. A single event can also start more than one sampler.
It uses the same local controls and status ring as the 1-channel gateway, so deployments can still be managed at the water's edge without relying on a phone or network connection.
The software layer the gateways report into. It is where rules are written, where device status is visible, and where the sample record lives.
The field interface for configuring devices, starting and checking runs, managing metadata and downloading data in the field.
A web overview of connected devices, used for remote monitoring, configuring rules and reviewing triggers.
The same principle, in air
Airborne molecular sampling can capture plant, fungal, pathogen and mammal signals moving through the surrounding environment, providing a wider picture of what is present across a landscape.
That changes what the right moment means. Detectability can depend on when sampling takes place and on the conditions at the time. A nocturnal species, for example, may be far less likely to appear in a sample collected only during the afternoon.
The same rules and triggers can therefore be applied to different conditions, such as day and night cycles, rainfall, weather or external data about what is happening in the surrounding area.
Start with the monitoring problem
If you can describe the signal, event or condition that matters, we can help define the sampling and monitoring approach around it.