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Posts from the ‘Soils’ Category

Complex Scientific Questions Yield Better Science in Desert FMP Project

The Desert FMP project originated from a discussion between pretty divergent scientists: Rick Gill, a BYU ecologist, another scientist who works on soil microbes, a plant physiologist, and a mammalogist who researches small mammals.

Desert FMP

Tree fire in Rush Valley

In an interview Rick said, “We started talking one day about the transformations that have occurred in the arid West over the past 100 years.  One of the things we are really interested in is fire.  How do ecosystems recover after fire? What’s the role of water in rangeland recovery? And the unique piece of this is: what’s the role of small mammals in this process?  We may never have thought of that question, or the complexity of researching how all of our questions work together in a system, if scientists from different disciplines hadn’t decided to collaborate.”

Desert FMP

Rush Valley research site. Five replications with four treatments: burned/unburned and small mammal/no small mammal. What’s interesting for us is that you can see that in the burned plots (the light brown) there are strong differences in the amount of the bright green plant—halogeton—that was present and it is systematically associated with the presence of small mammals. Here is the logic: In the spring, the presence of small mammals suppressed the cheatgrass and to some extent halogeton; in the absence of halogeton, cheatgrass ran wild. The cheatgrass transpired away all of the water and the halogeton that had germinated all died before it could flower.

As the experiment unfolds it is becoming clear that small mammals play a larger role in ecosystem recovery from fire than originally thought.  The scientists have used their observations to hypothesize that small mammals eat the seeds and seedlings of two invasive species. This ends up setting the vegetation along a very different trajectory than when small mammals are absent following fire.  Rick says, “We have discovered this complex but interesting interaction between water, fire, and small mammals. The first year after the fire, a really nasty range forb moved in called halogeton, which is toxic to livestock. Halogeton also accumulates salts in the upper soil profile that will cause failure in native plant germination.  Cheatgrass has also moved in which makes the area more prone to fire as it connects the sagebrush plants with flammable material. But what’s interesting is in treatments where mammals were present, the densities of both halogeton and cheatgrass were much lower than where small mammals were absent.

Desert FMP

Plot water potential comparison using matric potential sensors between Mammal (blue) and no mammal (red) over time. With no mammals to control cheatgrass, it depleted soil water availability below no mammal treatment and consequently halogeten was not able to grow.

 “The other really important thing is that cheatgrass and halogeton have different growth patterns.  Cheatgrass germinates in the Fall.  It reaches peak biomass early in the growing season and then dies off leaving a blanket of dead, highly flammable vegetation.  Halogeton germinates early in the growing season and remains relatively small until early Autumn when it bolts.  These are things that will be really easy to pick up using NDVI sensors, which are sensitive to the amount of green vegetation within the field of view of the sensor.  We are also using a system that we’ve designed to manipulate precipitation input.   This will enable us to connect water availability to the success of two invasive plants that have negative impacts on rangelands.  And with these same treatments we’re going to be able to tease out when in the year and to what extent small mammals are influencing the ecosystem by eating the seeds or the plant and at what stage.”

“Until I saw it in the field, the question of mammals being influential in rangeland fire recovery had never occurred to me.  We only discovered that piece of the puzzle because scientists from differing disciplines are working together.”

Below are two virtual tours of the site:

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Volumetric Water Content: Keeping your Eye on the Goal

Most scientists agree that it’s productive to attend seminars and conferences in order to talk with peers, share ideas, and learn about what other scientists are doing. However, in both academia and industry, we need to be careful that we are not so easily influenced by other scientists’ opinions that we lose sight of the end goals of our own projects. This happened to us recently at Decagon. Here’s the background: our volumetric water content (VWC) sensors actually measure the dielectric permittivity of the soil and use a transfer function to predict VWC from the measured dielectric value. Most of our sensors receive a “dielectric calibration” during the production process where they are calibrated in five dielectric standards to make sure they all measure dielectric permittivity accurately, thus leading to accurate VWC measurements with our standard transfer function.

volumetric water content

Volumetric water content (VWC) is determined by measuring the charge storing capacity of the soil using capacitance/frequency domain technology.

We were doing a pretty good job calibrating these sensors in dielectric standards, and our default dielectric-to-VWC transfer function resulted in good VWC accuracy. Then we went to a series of meetings and talked to some of our researcher friends who work on instrumentation. They said, “Look, your water sensors aren’t reading as accurately as they should in dielectric permittivity.” Here’s where the trouble started…

Wanting to make the perfect instrument, we went back and re-evaluated the dielectric calibration standards for these water content sensors and tried to use the book values of dielectric permittivity. This was a bad idea because it fundamentally changed the sensor output. Now, despite the sensors giving a slightly more accurate value for dielectric permittivity, they gave less accurate measurements of VWC. Compounding the problem, we now had a population of sensors that didn’t read the same as earlier sensors of the same type. So when customers started replacing their old sensors they said, “Wait a minute, this sensor reads 4% higher water content than my old water content sensor.” That’s when we realized that we had a real problem.

Our underlying mistake here is that we failed to remember that 99% of the people who buy our VWC sensors don’t even care what dielectric permittivity is. They just want an accurate, repeatable measurement of soil moisture. Essentially, because we were so focused on trying to produce a theoretically perfect sensor for a vocal minority of technically savvy users, we lost sight of the practical matter. Did our sensors produce an accurate water content measurement?

I wonder how often this happens in academia and industry. Scientists are bombarded with input from so many different stakeholders, it’s sometimes difficult to maintain the original focus of their projects. We need to remember to focus on the end goal and filter out things that may distract from that goal.

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Download the “Researcher’s complete guide to soil moisture”—>

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Spectral Reflectance and Water Content in the Wasatch Plateau Experiment

We chose to collaborate with Brigham Young University in an experiment on the Wasatch Plateau in 2009 because a scientist friend of ours had been working in that area the previous five years, and he noticed there were big grazing responses.  The plants growing in the long-term grazed areas were all drought tolerant, while ungrazed plots had plants that were often found only in wetter areas.  The only difference was the fence that kept sheep on one side and not on the other.   The big question was: how does water influence plants in this ecosystem that we understand relatively well? The story had always been the influence of grazers, when in fact, maybe the indirect consequence of grazing was mediated by water.

water

The Wasatch Plateau above Ephraim Canyon, UT, USA.

METER donated some sensors in order to set up an experiment where we changed the amount of water in various plots of land. We had rain exclusion plots, and we had treatments where we collected all incoming rainfall and reapplied it either once a week or every three weeks.   This allowed us to say to what extent this system was controlled by water during the growing season.  To do this, we took measurements with our prototype NDVI Spectral Reflectance Sensor to measure canopy greenness. We also used our prototype volumetric water content sensors to measure soil moisture (this was a few years ago and the sensors were prototypes at the time).  Using these sensors, we found that water is critical in a system people have dismissed as being climate-controlled because it’s at the top of a mountain.

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A very early prototype of a NDVI sensor measuring canopy greenness in experimental plots on the Wasatch Plateau.

It turns out the amount and timing of precipitation makes a big difference.  We were able to directly connect plant survival, not just to the grazing treatment, but to the actual amount of water that was in the soil. Also, using continuous NDVI data, we were able to look closely at the role of grazing on plant canopies.  When we looked at our NDVI data, we were able to see a seasonal signal, not just a single snapshot sample in time.  So by having the richer data from the data loggers, we obtained a more nuanced understanding of the impact of land use on these important ecological processes.

One of the mistakes we made was failure to include redundancy in the system.  We only had two replicates, so when one of them went down we ended up having just one little case study.  However, that mistake gave us new ideas on how to set up a better system using the right sensors for the job, and it generated a new idea on how to get real-time analysis of data.  In our new Desert FMP project, we have a much better-replicated system where more is invested in the number of sensors that we’re putting out. Each treatment combination will have five to ten water potential sensors.  We are also developing a system where we can analyze data in real-time, so this time we will know when a sensor goes out if a student accidentally kicks it.

 For more details on the Wasatch Plateau Experiment, watch for our published paper that we’ll link to when it comes out.

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