Long-term monitoring is one of the major pillars of ecological science. Yet, conducting long-term ecological research well is not obvious nor simple. It isn't as simple as "Just repeat."
I mentioned Dave Lindenmayer and Gene Likens' book on ecological monitoring. I don't often pick up books again after reading them, but did. I thought it deserved to be distilled down a bit more in my head.
When I think of the keys to long-term research, much of what they wrote resonated with ideas I've had.
There subsections were:
Good questions and evolving questions
The use of a conceptual model
Selection of appropriate entities to measure
Good design
Well-developed partnerships
Strong and dedicated leadership
Ongoing funding
Frequent use of data
Scientific productivity
Maintenance of data integrity and calibration of field techniques
Out of all of those, I think there are three points where things most often go wrong/are ignored:
First, test hypotheses. Monitoring can generate luck, but it's better to have a mental model of how ecosystems work. The long-term data should be used to test that. Do not just monitor a phenomenon, but also the potential underlying determinants. Stream NO3- might be your grand response, but have competing hypotheses about the factors that could be driving stream NO3-.
Second, be outside. Field stations are amazing places. Mostly because people are together looking at complex systems. Up time observing together and down time discussing observations are essential. No great program became great with people working in isolation from one another. Automated data collection is great only if it frees us up to spend the remainder of the time outside observing.
Third, analyze data annually. Don't let it accumulate. When I was doing weekly soil CO2 flux data at Cedar Creek, I analyzed the data that night. Just to see what the pattern was and make sure nothing went bonkers that day. It takes practice to generate (new) hypotheses and be ready for surprises. Groups need to come together to compare trends frequently. If you are not analyzing and discussing your data annually, you're not doing it right. Long-term data analysis is a process, not an event.
Some advances are data driven. Others wisdom-driven. To improve long-term research, go no further than these three points.
I mentioned Dave Lindenmayer and Gene Likens' book on ecological monitoring. I don't often pick up books again after reading them, but did. I thought it deserved to be distilled down a bit more in my head.
When I think of the keys to long-term research, much of what they wrote resonated with ideas I've had.
There subsections were:
Good questions and evolving questions
The use of a conceptual model
Selection of appropriate entities to measure
Good design
Well-developed partnerships
Strong and dedicated leadership
Ongoing funding
Frequent use of data
Scientific productivity
Maintenance of data integrity and calibration of field techniques
Plus a section entitled "Little things matter a lot! Some tricks of the trade": field transport, field staff, access to field sites, time in the field,
Out of all of those, I think there are three points where things most often go wrong/are ignored:
First, test hypotheses. Monitoring can generate luck, but it's better to have a mental model of how ecosystems work. The long-term data should be used to test that. Do not just monitor a phenomenon, but also the potential underlying determinants. Stream NO3- might be your grand response, but have competing hypotheses about the factors that could be driving stream NO3-.
Second, be outside. Field stations are amazing places. Mostly because people are together looking at complex systems. Up time observing together and down time discussing observations are essential. No great program became great with people working in isolation from one another. Automated data collection is great only if it frees us up to spend the remainder of the time outside observing.
Third, analyze data annually. Don't let it accumulate. When I was doing weekly soil CO2 flux data at Cedar Creek, I analyzed the data that night. Just to see what the pattern was and make sure nothing went bonkers that day. It takes practice to generate (new) hypotheses and be ready for surprises. Groups need to come together to compare trends frequently. If you are not analyzing and discussing your data annually, you're not doing it right. Long-term data analysis is a process, not an event.
Some advances are data driven. Others wisdom-driven. To improve long-term research, go no further than these three points.
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