For class this week we practiced the use of radiotelemetry techniques for locating animals. Last week a graduate class tagged three grey catbirds with radio-transmitters for our assignment. After a briefing, our class was divided into groups and tasked with approximating the location of our assigned catbird using radiotelemetry. We were given four points around campus to scan for signals, a receiver and antenna, and a compass. And out we went. We loaded up into the vans and made our way to the points.
The weather that day was not exactly conducive for radio-tracking; the fog was brutal. What are typically serine mountain views were an empty white canvas of fog. Extremely dense fog can make it difficult to receive radio signals, so naturally we were off to a bad start. However the fog did create one benefit: songbirds can be very mobile but bad weather tends to restrict their movements. (When using radiotelemetry you want your subject to stay in one place.)
So we pushed on! We were able to receive three signals which is the absolute minimum needed to triangulate an animal's location. We took our findings back to the computer lab and laid the data out on the map. It was a bust! Our data didn’t make any sense! (Or so we thought.) The lines didn’t lead to one concise location like they were supposed to.
Rather than give up out of frustration, we came up with a plan. Another group had taken signal directions for our bird at nearly the same times, but in different locations. We combined our data and looked at it chronologically. And all of a sudden, it made perfect sense! We had three lines at the beginning of the morning that all pointed off in the same direction. But half way through sampling they all switched and triangulated in a patch of forest. The bird had moved! Fancy that.
What I learned is that sometimes your results may seem useless, but there is almost always an explanation. Sometimes you just need more data and a new perspective. While GPS tracking is becoming more popular in field ecology, radiotelemetry is still an extremely important tool. In the end, I was actually glad that the data made me work for my results. In the real world, nature doesn’t always cooperate but you have to figure it out.
