Despite Drawbacks, Scientific Collaboration Pays Off
Though collaboration can fuel innovation and increase the relevance and complexity of the scientific questions we study, I’ve noticed it does have its ups and downs. The highs and lows we’ve run into on our research projects may help others avoid some of the pitfalls we experienced as many diverse groups tried to learn how to work together.
Researchers discussing science at the Lytle Ranch Preserve, a remarkable desert laboratory located at the convergence of the Great Basin, Colorado Plateau, and Mojave Desert biogeographical regions.
There can be bumps in the road when collaborating with companies who want to test their product. Being at the forefront of innovation means that untested sensors may require patience as you work out all the bugs together. But from my perspective, this is part of the fun. If we are late adopters of technology, we wouldn’t get to have a say in creating the sensors that will best fit our projects as scientists.
Collaborating scientists can also sometimes run into problems in terms of the stress of setting up an experiment in the time frame that is best for everyone. During our experiment on the Wasatch Plateau, we had six weeks to get together soil moisture and water potential sensors, but our new GS3 water content, temperature, and EC sensors had never been outside of the lab. In addition, we planned to use an NDVI sensor concept that came out of a workshop idea my father Gaylon had. We’d made ONE, and it seemed to work, but that is a long way from the 20 we needed for a long-term experiment in a remote location at 3000 meters elevation. In the end, it all worked out, but not without several late nights and a bit of luck. I remember students holding jackets over me to protect me from the rain as I raced to get the last sensor working. Then we shut the laptop and ran down the hill, trying to beat a huge thunderstorm that started to pelt the area.
Desert-FMP Researchers at the Lytle Ranch Preserve
Other challenges of scientific collaboration present organizational hardships. One of the interesting things about the interdisciplinary science in the Desert FMP project is the complexity of the logistics, and maybe that’s a reason why some people don’t do interdisciplinary projects. We are finding in order to get good data on the effects of small mammals and plants you need to coordinate when you are sampling small mammals and when you’re sampling plants. Communicating between four different labs is complicated. Each of the rainout shelters we use cover an area of approximately 1.5 m2 . That’s not a lot of space when we have two people interested in soil processes and two people interested in plants who all need to know what’s going on underneath the shelter. Deciding who gets to take a destructive sample and who can only make measurements that don’t change the system is really hard. The interesting part of the project where we’re making connections between processes has required a lot of coordination, collaboration, and forward-thinking.
In spite of the headaches, my colleague and I continue to think of ways we can help each other in our research. Maybe we’re gluttons for punishment, but I think the benefits far outweigh the trouble we’ve had. For instance, in the above-mentioned Desert FMP project we’ve been able to discover that small mammals are influential in rangeland fire recovery (read about it here). We only discovered that piece of the puzzle because scientists from differing disciplines are working together. In our Wasatch Plateau project, my scientist colleague said it was extremely helpful for him to be working with an instrumentation expert who could help him with setup and technical issues. Also, we’ve been able to secure some significant grants in our Cook Farm Project (you can read about it in an upcoming post) and answer some important questions that wouldn’t have occurred to either one of us, if we hadn’t been working together. In addition, solving problems that have cropped up in our projects has spurred us on to a new idea for analyzing enormous streams of data in near-real time. (read about it here).
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