The document investigates interactions between two grass species (blue grama and black grama) and soil crust cyanobacteria in Chihuahuan Desert grassland. It finds that the two grass species inhabit distinct soil environments but soil traits are poor predictors of cyanobacterial density. Cyanobacterial density decreased after rainfall, possibly due to erosion. While the grasses occupy different soil patches, their root systems may not differ enough to strongly influence cyanobacterial densities as plant species did not predict cyanobacterial levels. Overall abiotic soil/climate factors rather than plant species appear to be stronger drivers of cyanobacterial densities in this ecosystem.
1. Interactions among Bouteloua Grasses, Soil
Type, Moisture, and Crust Cyanobacteria in
Chihuahuan Desert Grassland
Roy Z Moger-Reischer
rmogerr2@u.rochester.edu
2. Acknowledgements
• Y. A. Chung, J. A. Rudgers, E. Dettweiler-
Robinson, B. Haley, M. R. Kazenel
• R. L. Sinsabaugh
4. Biocrust-plant interactions
•Positive and negative impacts; bidirectional
•Interaction pathways unclear
+ Increased nutrients
+ Increased water availability
- Toxic compounds
- Direct competition
5. Bouteloua spp.
• Similar traits and environmental tolerances
• Different root systems—disparate ability to
interact with biocrust cyanobacteria?
Blue grama—bunchgrass,
adventitious roots
Black grama—rhizomatous roots
6. Motivation
• Is there a spatial association between
biocrust cyanobacteria abundance and
grass species?
• H1: Plant species is a stronger driver of
biocrust cyanobacterial density.
• H2: Abiotic factors (soil traits;
precipitation) are stronger drivers of
biocrust cyanobacterial density.
7. Methods
• Establish twenty 1 m x 2 m plots along Deep Well Transect
• Per plot:1 blue grama,1 black grama focal plant; 20 pairs of focal plants total
• Measure soil texture (%sand, %silt, %clay); temperature; moisture
8. •Blue
•Black
MANOVA: Soil PC1 & PC2 vs. plant species
•Soil type clouds not significantly overlapping (p < 0.003)
•Black grama: Higher moisture, temperature, %silt
10. • Do different plant species associate with
biocrusts of different cyanobacterial
densities?
• H1: Plant species is a stronger driver of
biocrust cyanobacterial density.
• H2: Abiotic factors (soil traits;
precipitation) are stronger drivers of
biocrust cyanobacterial density.
17. Take-home ideas
Black grama and blue grama inhabit distinct patch-
scale soil environments.
Soil traits are poor predictors of biocrust
cyanobacterial density
Cyanobacterial density ↓ immediately after first
monsoon rain event
18. • Do different plant species associate with
biocrusts of different cyanobacterial
densities?
• H1: Plant species is a stronger driver of
biocrust cyanobacterial density.
• H2: Abiotic factors (soil traits;
precipitation) are stronger drivers of
biocrust cyanobacterial density.
19. Plant species and [chl a]
Slightly more cyanobacteria for black grama; not
statistically significant (P = 0.58)
[chla](μg/g)
20. Plant species and [chl a]
Slightly more cyanobacteria for black grama; not
statistically significant (P = 0.58)
Blue
grama
Black
grama
21. Take-home ideas
Black grama and blue grama inhabit distinct patch-
scale soil environments.
Soil traits are poor predictors of biocrust
cyanobacterial density
Cyanobacterial density ↓ immediately after first
monsoon rain event
Erosion? Ecotone shift?
No [chl a] difference among species root
systems not very different/ not critical interaction
pathway?
22. Figure 3: Biplot of PCs 1 & 2
Biplot: samples depicted as points, variables as vectors. Points 1-20 indicate BOER
individuals, 21-40 BOGR. The -40 – 60 x- and y-axes indicate their PC1 and PC2 scores,
respectively. The -0.2 – 0.4 axes represent rescaled values for the factor loadings. The large
x-component of the moisture vector shows that PC1 comprises primarily variation in
moisture. Similarly, sand and clay variation influence PC2. The dominance of moisture
reflects its large coefficient of variation (CV).
25. Full linear model
1. Moisture
2. Temperature
3. Texture
4. (plant species)
Multiple regression to predict [chl a]
26. Full linear model
1. Moisture
2. Temperature
3. Texture
4. (plant species)
Adjusted R2: -0.06
Overall p-value: 0.73
Lowest AIC model: [chl a] = CONSTANT
27. Take-home ideas
Black grama and blue grama inhabit distinct patch-
scale soil environments.
Soil traits are poor predictors of biocrust
cyanobacterial density
Cyanobacterial density ↓ immediately after first
monsoon rain event
Erosion? Ecotone shift?
No [chl a] difference among species root
systems not very different/ not critical interaction
pathway?
28. Motivation
• Do different plant species associate with
biocrusts of different cyanobacterial
densities?
• H1: Plant species is a stronger driver of
biocrust cyanobacterial density.
• H2: Abiotic factors (soil traits;
precipitation) are stronger drivers of
biocrust cyanobacterial density.
Editor's Notes
Chihuahuan Desert is what we have here at the Sev. BOER and BOGR dominate—yes, most people call them Black and Blue grama, but I am trying to usher you into plant ecologist-world, so I will use our codenames. We know they coexist and interact with BSCs—but previous studies looked mostly at SOIL responses (eg CO2, N2O production); extracellular enzyme activity. They have tended to focus on germination and seedling establishment stages of the plant Few have examined BOTH plant and BSC fitness responses.
Given the diversity of ways in which BSCs can engineer their biotic and abiotic environment, it is no surprise that they can have fitness impacts on surrounding vascular plants.
Allelopathy---can secrete poisonous secondary metabolites?
Direct competition for nutrients?
There may be more interactions waiting to be discovered. What are the pathways? Could the root system be highly important?
Form: Perennial, tufted C4 grasses Prefer full sun, xeric conditions differ in their root system: Blue is a ring-forming tussock grass. Black is rhizomatous. Since root systems are integral to a grass’s association with circumambient soil and BSC, this critical difference could influence the distribution and density of cyanobacteria in the crusts. Blue’s roots are more extensive, so a maybe denser cyanobacts near blue overall positive fitness impact on BSC
This study addressed: (1) Given the different root systems, do BOER and BOGR associate differently with cyanobacterial soil crusts? (2) What is the relative efficacy of abiotic soil metrics to predict BSC cyanobacteria density, compared to that of biological variables—We also have to account for soil chemical traits, soil texture, etc, as possible explanatory metrics
Tao Te Ching verse 11 ends: “A pot is made of clay, but the hollow inside is what is useful. Door, windows, and walls build a house, but we live in the empty space within. Thus the importance of what is depends on what is not.”
The interspaces are often occupied by biological soil crusts—microbial communities which bind soil particles to create a living crust.
They cover up to 70% of the desert. While visually the “what is not”, crusts may be very important.
BSCs can be composed of lichens, mosses or cyanobacts. I am focusing on cyanobacts. They are keystone members of arid communities: perform primary productivity; regulate nutrient cycling; change soil [N] & [NH4] thru N-fixation or nitrogenase; regulate water cycling (and, cool thing, they have special mechanisms to survive desiccation); incr soil pH, conductivity;
But there were some interesting results in plant-world:
We ran MANOVA of PCs 1 & 2 against plant species, and found highly significant differences (P < .005)
Red cluster of BOGR—Blue grama has lower PC1 scores
1) Maybe the root system isn’t important to interaction. Or, another study (Coffin & Lauenroth 1991) on Blue grama found that > 75% of its roots extend no further than 5 cm from the plant—so the root diffs may not have much influence 2) In a study in the Negev (Johnson et al 2012), there was also a ~15 – 30% decrease in [chl a] after a rainstorm in a DRY period; thus not unprecedented. Possibilities: i) cells burst from osmotic pressure ii) dryness renders crust susceptible to erosion from rain iii) increased soil temp + sudden moisture can slow growth or kill cyanobacts. So w/ climate change incr temp & making precip patterns erratic, there are dire implications for BSCs and the organisms that rely on them. Also introduces the idea of an optimum lag time for maximal crust collection efficiency 3)In 2004 McK Flats were sampled as Sandy Loam or Loamy Sand, > 64% Sand. I found all were SCL, >56% 4) Black grama in warmer, wetter soil due to its tendency to start later in the growing season? Blue grama in dry soil long-lived (400 vs 40 yr) better able to withstand desiccation. They decrease competition by specializing in mini-niches 5) Climate change models predict that in the altered conditions, codominance will yield to Black dominance. The BOER/BOGR ecotone will move northward. Could be unfortunate b/c Blue is better for erosion control & is less susceptible to overgrazing.
This study addressed: (1) Given the different root systems, do BOER and BOGR associate differently with cyanobacterial soil crusts? (2) What is the relative efficacy of abiotic soil metrics to predict BSC cyanobacteria density, compared to that of biological variables—We also have to account for soil chemical traits, soil texture, etc, as possible explanatory metrics
Basically just poked a 1.5 mL eppendorf tube into the soil, 5 – 10 mm deep, within 5 cm of each focal plant, and inverted it to scoop up a bit of crust. I sampled both before rain and after the monsoon.
1) Maybe the root system isn’t important to interaction. Or, another study (Coffin & Lauenroth 1991) on Blue grama found that > 75% of its roots extend no further than 5 cm from the plant—so the root diffs may not have much influence 2) In a study in the Negev (Johnson et al 2012), there was also a ~15 – 30% decrease in [chl a] after a rainstorm in a DRY period; thus not unprecedented. Possibilities: i) cells burst from osmotic pressure ii) dryness renders crust susceptible to erosion from rain iii) increased soil temp + sudden moisture can slow growth or kill cyanobacts. So w/ climate change incr temp & making precip patterns erratic, there are dire implications for BSCs and the organisms that rely on them. Also introduces the idea of an optimum lag time for maximal crust collection efficiency 3)In 2004 McK Flats were sampled as Sandy Loam or Loamy Sand, > 64% Sand. I found all were SCL, >56% 4) Black grama in warmer, wetter soil due to its tendency to start later in the growing season? Blue grama in dry soil long-lived (400 vs 40 yr) better able to withstand desiccation. They decrease competition by specializing in mini-niches 5) Climate change models predict that in the altered conditions, codominance will yield to Black dominance. The BOER/BOGR ecotone will move northward. Could be unfortunate b/c Blue is better for erosion control & is less susceptible to overgrazing.
In general, [chl] was very very low—typically less than 0.6 MICROgrams per g soil, i. e. point out the y-axis
Surprisingly, the total chl density was LOWER after the rain
1) Maybe the root system isn’t important to interaction. Or, another study (Coffin & Lauenroth 1991) on Blue grama found that > 75% of its roots extend no further than 5 cm from the plant—so the root diffs may not have much influence 2) In a study in the Negev (Johnson et al 2012), there was also a ~15 – 30% decrease in [chl a] after a rainstorm in a DRY period; thus not unprecedented. Possibilities: i) cells burst from osmotic pressure ii) dryness renders crust susceptible to erosion from rain iii) increased soil temp + sudden moisture can slow growth or kill cyanobacts. So w/ climate change incr temp & making precip patterns erratic, there are dire implications for BSCs and the organisms that rely on them. Also introduces the idea of an optimum lag time for maximal crust collection efficiency 3)In 2004 McK Flats were sampled as Sandy Loam or Loamy Sand, > 64% Sand. I found all were SCL, >56% 4) Black grama in warmer, wetter soil due to its tendency to start later in the growing season? Blue grama in dry soil long-lived (400 vs 40 yr) better able to withstand desiccation. They decrease competition by specializing in mini-niches 5) Climate change models predict that in the altered conditions, codominance will yield to Black dominance. The BOER/BOGR ecotone will move northward. Could be unfortunate b/c Blue is better for erosion control & is less susceptible to overgrazing.
This study addressed: (1) Given the different root systems, do BOER and BOGR associate differently with cyanobacterial soil crusts? (2) What is the relative efficacy of abiotic soil metrics to predict BSC cyanobacteria density, compared to that of biological variables—We also have to account for soil chemical traits, soil texture, etc, as possible explanatory metrics
It turned out that, both pre- and post-monsoon, the cyanobacteria were distributed essentially randomly with respect to plant species.
(And the grph is for post)
It turned out that, both pre- and post-monsoon, the cyanobacteria were distributed essentially randomly with respect to plant species.
(And the grph is for post)
1) Maybe the root system isn’t important to interaction. Or, another study (Coffin & Lauenroth 1991) on Blue grama found that > 75% of its roots extend no further than 5 cm from the plant—so the root diffs may not have much influence 2) In a study in the Negev (Johnson et al 2012), there was also a ~15 – 30% decrease in [chl a] after a rainstorm in a DRY period; thus not unprecedented. Possibilities: i) cells burst from osmotic pressure ii) dryness renders crust susceptible to erosion from rain iii) increased soil temp + sudden moisture can slow growth or kill cyanobacts. So w/ climate change incr temp & making precip patterns erratic, there are dire implications for BSCs and the organisms that rely on them. Also introduces the idea of an optimum lag time for maximal crust collection efficiency 3)In 2004 McK Flats were sampled as Sandy Loam or Loamy Sand, > 64% Sand. I found all were SCL, >56% 4) Black grama in warmer, wetter soil due to its tendency to start later in the growing season? Blue grama in dry soil long-lived (400 vs 40 yr) better able to withstand desiccation. They decrease competition by specializing in mini-niches 5) Climate change models predict that in the altered conditions, codominance will yield to Black dominance. The BOER/BOGR ecotone will move northward. Could be unfortunate b/c Blue is better for erosion control & is less susceptible to overgrazing.
PC1 was 85% of the variance, PC2 was 13%...
The factor loadings tell the magnitude and direction of the how the variables went into each PC.
The proportion of variance tells how much of the overall variance that partic PC explains. Note the numbers in red.
1) Maybe the root system isn’t important to interaction. Or, another study (Coffin & Lauenroth 1991) on Blue grama found that > 75% of its roots extend no further than 5 cm from the plant—so the root diffs may not have much influence 2) In a study in the Negev (Johnson et al 2012), there was also a ~15 – 30% decrease in [chl a] after a rainstorm in a DRY period; thus not unprecedented. Possibilities: i) cells burst from osmotic pressure ii) dryness renders crust susceptible to erosion from rain iii) increased soil temp + sudden moisture can slow growth or kill cyanobacts. So w/ climate change incr temp & making precip patterns erratic, there are dire implications for BSCs and the organisms that rely on them. Also introduces the idea of an optimum lag time for maximal crust collection efficiency 3)In 2004 McK Flats were sampled as Sandy Loam or Loamy Sand, > 64% Sand. I found all were SCL, >56% 4) Black grama in warmer, wetter soil due to its tendency to start later in the growing season? Blue grama in dry soil long-lived (400 vs 40 yr) better able to withstand desiccation. They decrease competition by specializing in mini-niches 5) Climate change models predict that in the altered conditions, codominance will yield to Black dominance. The BOER/BOGR ecotone will move northward. Could be unfortunate b/c Blue is better for erosion control & is less susceptible to overgrazing.
This study addressed: (1) Given the different root systems, do BOER and BOGR associate differently with cyanobacterial soil crusts? (2) What is the relative efficacy of abiotic soil metrics to predict BSC cyanobacteria density, compared to that of biological variables—We also have to account for soil chemical traits, soil texture, etc, as possible explanatory metrics
