Hydric soils form when anaerobic conditions develop under sustained saturation (via flooding or ponding) during the growing season.
Hydric soils occur across the United States. From coastal Oregon soils to temperate wetlands in Minnesota to southern pine forests, these biologically significant soils develop across a variety of climates, topographies, vegetation, and parent materials.
What drives oxygen depletion?
Study areas were selected using Web Soil Survey, SoilWeb, and NASIS to find geographically distinct areas that contained hydric soils.
For this project, we will explore hydric soils in six geographically contrasting wildland sites:
| Site | State | Soil Survey Area | MLRA |
|---|---|---|---|
| Siuslaw National Forest | Oregon | OR638 | 4A |
| Battleground State Park | Minnesota | MN021 | 93A |
| Snake River Valley | Wyoming | WY666 | 43B |
| Targhee National Forest | Idaho | ID758 | 43B |
| De Soto National Forest | Mississippi | MS131 | 133C |
| Yellow River Wildlife Management Area | Florida | FL113 | 133C |
Overarching Objective: Investigate drivers of redox conditions in hydric soils.
Hypotheses
Hypothesis 1: Water table depth and perched water tables will result in aquic conditions and redox features.
Hypothesis 2: Higher organic carbon content drives aerobic metabolism via substrate availability, resulting in oxygen consumption and anaerobic conditions.
First, we need to compare the amount of hydric soils at each site. Here, the term site is used to reference each individual study area which represents a selected soil survey area. See site table for those individual soil survey areas. Relevant forests included within the soil survey areas are used in site terminology, as they are the locations I was targeting when selecting the soil survey areas.
To do this, we subset our dataset to select only soils that are some percent hydric (greater than zero).
The sites all have different amounts of pedons that are > 0 % hydric. From most soil with hydric soils to the least:
Now, it is immediately apparent that some amount of this separation in amount of hydric soils across sites is accounted for by climate.
But, before diving too much further into how these sites are different, let’s look at what common drivers in redox may be occurring across all of these sites.
Subset soils from the extracted NASIS pedons that include one of the following
Of the remaining pedons across all the sites, plot the clay, pH, and gravel
Here we assess pedons from our four lowest hydric pedon count site areas. We are looking for any pedons that contain a diagnostic feature that we associate with redox conditions.
Our analysis only yields pedons from Mississippi, Oregon, and Wyoming. No soils from the Florida site met our criteria, likely because there is an usually high proportion of soils that have NA data.
In the table below, soils are listed in the same order as the figure (left to right).
| Soil Series | SSA | Aquic Conditions | Reduced Matrix | Redox Conc. | Redox Depl. |
|---|---|---|---|---|---|
| Saucier | MS131 | X | |||
| Coquille | OR041 | X | X | ||
| Wilsonville | WY039 | X | |||
| Chitwood | OR041 | X | |||
| Chitwood | OR041 | X | |||
| Coquille | OR041 | X | X | X | X |
Please note that the two Oregon soil series have two contrasting pedons each, which is the reason you must refer to the figure to understand how they are listed in the table. In the case of Chitwood, the soil series are identical, and I’m not sure what differentiates them
The Saucier soil is an interesting one to use to test our hypotheses (keeping in mind that this is a limited analysis and may over interpret the data).
While aquic conditions occur, likely as a combination of climate and a shallow water table associated with plinthite, there appears to be little organic content (as far as we can tell from this data). That could indicate that while saturation is present, oxygen consumption is limited due to constraints in substrate availability.
I would expect the Oregon soils to have more organic carbon, based on the climate. But we can’t know for sure just by looking at these profiles.
So…
I guess we better go look at some KSSL data!
Oh wow!
It looks like Saucier is, indeed, very low in organic carbon content throughout the profile.
We can compare with Oregon’s Coquille which does indeed have quite a bit more organic carbon.
While we’re here, let’s take a look at clay too!
Definitely a lot of clay in those Missippi soils. Also quite a bit in Chitwood, the other Oregon soil. That makes sense, there are definitely some ultisols up there!
Let’s do another subset of our data!
There are 765 pedons that are greater than zero percent hydric.
nrow(mu_dat)
## [1] 765
Of those 765 pedons that have some amount of hydric soil, the majority are less than 25. The next highest frequency of soils that are some percent hydric occurs at the opposite end of the spectrum (over 75 % hydric).
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
In order to do a more targeted analysis, we will subset the dataset to focus on soils most likely to be hydric. To create this subset, we will select only soils that are > 75 percent hydric.
Look at these gorgeous soil profiles. Notably, Atmore has plinthite, did our other MS soil: Saucier.
Rains is our Florida soil, and one of the most reduced. This aligns with what I would expect from a swampy Florida soil! Those NAs in our earlier dataset may have done us a big disservice in finding a cool reduced Florida soil!
Of these soils, Rifle, Mooseflat, and Brenner seem likely to have the most organic matter.
Interesting. Rifle and Mooseflat (MN and ID, respectively) are some of the coldest soils (along with WY Newfork). So, does this mean we’re accumulating organic matter? Are we limiting microbial metabolism in colder temperatures or providing more substrate to decompose and consume oxygen? Rifle and Mooseflat are the only soils with an organic horizon … or six organic horizons, in Rifle’s case.
It’s a question I run into a lot in my research, and I still don’t know how to account for temperature in the microbial oxygen consumption and substrate generation balance.
However, it’s worth noting that ID and MN had the most > 75 % hydric soils of all site areas.
Look at the Mediterranean climate represented by Brenner along the Oregon Coast!
The timing of precipitation may matter as well, which is one reason I love these plots. In Atmore and Brenner, the majority of precipitation falls in the summer: the hottest months. That’s also when Potential ET is highest. Is that why FL and MS were on the lower end of > 75 % hydric soils? Does that, along with lack of substrate availability, limit redox fluctuations?
No surprise that toeslopes are dominating these > 75 % hydric soils!
Rains is interesting, with the summit position’s representation. And yet, that soil was quite gleyed! However, if we take a look at the map, we can see that Rains is a widespread soil… so we’re representing quite a few pedons here!
As with all studies, this one raises more questions than it answers.
My conclusion is that the factors that drive redox conditions vary geographically!
If only I could install redox probes in every soil in the US! We would see some very interesting seasonal dynamics.
For now, these are the key concepts that I plan to think about more:
Evapotranspiration and low substrate availability may be key constraining factors in hydric soil development in southeastern coastal and forested soils.
Colder temperatures may not slow down microbial consumption of oxygen when generous amounts of substrate are on the table.
What is the tipping point between aquic conditions and hydric soil occurrence? In the case of Saucier, it would be interesting to understand what is controlling oxygen consumption and saturation. Does the seasonality of that saturation influence residence time, if saturation coincides with high evapotranspiration?
## 'data.frame': 80 obs. of 12 variables:
## $ series : chr "ATMORE" "ATMORE" "ATMORE" "ATMORE" ...
## $ climate_var: chr "Effective Precipitation (mm)" "Mean Annual Air Temperature (degrees C)" "Elevation (m)" "Fraction of Annual PPT as Rain" ...
## $ minimum : num 434 18 0 100 1384 ...
## $ q01 : num 521.3 18.2 2 100 1481.9 ...
## $ q05 : num 542.8 18.3 4 100 1505 ...
## $ q25 : num 575.5 18.5 19 100 1536 ...
## $ q50 : num 607.1 18.7 40 100 1583 ...
## $ q75 : num 641.9 19.4 59 100 1653 ...
## $ q95 : num 672.9 19.6 83 100 1706 ...
## $ q99 : num 702.3 19.6 117.2 100 1750.1 ...
## $ maximum : num 751.4 19.7 155 100 1791 ...
## $ n : int 4890 4890 4890 4890 4890 4890 4890 4890 278 278 ...
## Design Freeze Index (degrees C) Effective Precipitation (mm)
## ATMORE 26 607.09216
## BRENNER 27 1463.27740
## CHITWOOD 27 1584.73230
## COQUILLE 17 1248.32629
## MOOSEFLAT 1143 -15.84467
## NEWFORK 988 -34.68781
## Elevation (m) Fraction of Annual PPT as Rain Frost-Free Days
## ATMORE 40.0 100 254
## BRENNER 35.5 99 246
## CHITWOOD 36.0 99 242
## COQUILLE 10.0 99 265
## MOOSEFLAT 1962.0 78 75
## NEWFORK 2435.0 77 87
## Growing Degree Days (degrees C)
## ATMORE 3529.0
## BRENNER 1161.5
## CHITWOOD 1141.0
## COQUILLE 1142.5
## MOOSEFLAT 861.5
## NEWFORK 995.0
## Mean Annual Air Temperature (degrees C)
## ATMORE 18.735134
## BRENNER 11.018567
## CHITWOOD 10.980069
## COQUILLE 10.993950
## MOOSEFLAT 2.845994
## NEWFORK 3.674289
## Mean Annual Precipitation (mm)
## ATMORE 1583.0
## BRENNER 2129.5
## CHITWOOD 2248.0
## COQUILLE 1891.0
## MOOSEFLAT 433.0
## NEWFORK 438.0
## Dissimilarities :
## ATMORE BRENNER CHITWOOD COQUILLE MOOSEFLAT NEWFORK RAINS RIFLE
## BRENNER 3.35
## CHITWOOD 3.56 0.32
## COQUILLE 3.08 0.63 0.94
## MOOSEFLAT 6.22 6.01 6.21 5.75
## NEWFORK 6.23 6.13 6.32 5.86 0.66
## RAINS 1.06 3.64 3.90 3.26 5.53 5.56
## RIFLE 5.26 5.01 5.21 4.72 2.47 2.98 4.65
## SAUCIER 0.31 3.48 3.67 3.23 6.47 6.47 1.31 5.51
## WILSONVILLE 6.54 6.25 6.41 6.04 0.98 1.16 5.94 2.76
## SAUCIER
## BRENNER
## CHITWOOD
## COQUILLE
## MOOSEFLAT
## NEWFORK
## RAINS
## RIFLE
## SAUCIER
## WILSONVILLE 6.77
##
## Metric : euclidean
## Number of objects : 10
## Initial stress : 0.01980
## stress after 10 iters: 0.00453, magic = 0.068
## stress after 20 iters: 0.00219, magic = 0.500
## stress after 30 iters: 0.00215, magic = 0.500
## Run 0 stress 0.001634267
## Run 1 stress 0.001691795
## ... Procrustes: rmse 0.02613558 max resid 0.03819425
## Run 2 stress 0.001377214
## ... New best solution
## ... Procrustes: rmse 0.02259575 max resid 0.03500646
## Run 3 stress 0.0002928714
## ... New best solution
## ... Procrustes: rmse 0.03756081 max resid 0.05819401
## Run 4 stress 0.0009580848
## Run 5 stress 0.002093324
## Run 6 stress 0.002059344
## Run 7 stress 0.001460113
## Run 8 stress 0.002818151
## Run 9 stress 9.735401e-05
## ... New best solution
## ... Procrustes: rmse 0.02134815 max resid 0.03869611
## Run 10 stress 0.143587
## Run 11 stress 0.001732311
## Run 12 stress 0.002576199
## Run 13 stress 0.00205349
## Run 14 stress 0.002140367
## Run 15 stress 0.00145642
## Run 16 stress 0.143587
## Run 17 stress 0.000672715
## Run 18 stress 0.002565471
## Run 19 stress 0.001116948
## Run 20 stress 9.929305e-05
## ... Procrustes: rmse 0.0169244 max resid 0.0324833
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 16: no. of iterations >= maxit
## 2: stress < smin
## 2: scale factor of the gradient < sfgrmin
## Warning in metaMDS(sp4.scaled[, -1], distance = "gower", autotransform = FALSE,
## : stress is (nearly) zero: you may have insufficient data
## species scores not available
## species scores not available
Interesting! Oregon coast and Gulf coast had similar climates while Minnesota and Wyoming/Idaho had similar climates. Those similarities did not translate to similar redox conditions!
It’s interesting to consider which climate parameters are most similar across these dendrogram specified groupings (design freeze index, elevation, MAP) and which are not similar (effective precip, growing degree days, MAAT). What do these similarities and differences tell us about climate controls on redox conditions?
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