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Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California
<div><em><strong>Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California</strong></em></div> <div> </div> <div>Supporting data for “Landscape transience reveals a bottom-up control on soil production”</div> <div> </div> <div>Emily C. Geyman*, David A. Paige, Michael P. Lamb</div> <div> </div> <div>*Corresponding author: Emily C. Geyman, egeyman@caltech.edu</div> <div> </div> <div>Last updated: July 3, 2024</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div><strong>Dataset overview.</strong></div> <div> </div> <div>This dataset contains:</div> <div> </div> <div>1. Georeferenced TIFF files of the (i) LiDAR-derived surface elevation, (ii) geological map (based on the mapping from Dibblee (1973) and Arrowsmith (1995)), (iii) reconstructed cumulative uplift, and (iv) reconstructed uplift rate at the Dragon’s Back Pressure Ridge, Carrizo Plain, California.</div> <div> </div> <div>2. Raw and processed ground penetrating radar (GPR) observations of soil thickness.</div> <div> </div> <div>3. Geomorphic properties: (i) hilltop erosion rate, (ii) hilltop soil production rate, (iii) hilltop saprolite weakness (based on cone penetrometer observations), and (iv) hilltop soil thickness.</div> <div> </div> <div>4. Raw and processed observations from the cone penetrometer (used to compute the saprolite weakness).</div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>6. Matlab code used to perform the MCMC inversion to generate the uplift reconstructions (item (1) above).</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>Dataset details.</strong></div> <div> </div> <div>See the publication: “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” for details on the field methodology and data analysis. Details about each data product also are provided below. </div> <div> </div> <div> </div> <div>1. Geotiffs.</div> <div> </div> <div>We provide georeferenced TIFF files of the (i) surface elevation, (ii) geological map, (iii) cumulative uplift, and (iv) uplift rate at Dragon’s Back Pressure Ridge, Carrizo Plain, California. The coordinate system for the geotiffs is WGS84 / UTM Zone 11 N (EPSG:32611). All geotiffs are provided at 0.5 m x 0.5 m spatial resolution. Details about each dataset are provided below.</div> <div> </div> <div>(i) Surface elevation. We use LiDAR data from the 2005 B4 Lidar Project, acquired and processed by the National Center for Airborne Laser Mapping (NCALM). The full LiDAR dataset is available for download from OpenTopography (https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032018.32611.1). We convert the LiDAR point cloud to a 0.5 m gridded bare earth digital elevation model (DEM). </div> <div> </div> <div>(ii) Geological map. The original geological map of Dibblee (1973, 1999) is available from the United States Geological Survey (USGS) at https://pubs.usgs.gov/of/1999/of99-014/. This mapping was refined by Arrowsmith (1995) and Hilley & Arrowsmith (2008). We modify the geological map using high-resolution satellite imagery (from Google, ESRI, and Bing mosaics), as well as high-resolution imagery from the National Agriculture Imagery Program (NAIP), in order to follow the contacts of the Pink, Tan, and Gray members of the Paso Robles Formation. The units on the geological map are coded as:</div> <div>1 - Pink Member, Paso Robles Formation</div> <div>2 - Tan Member, Paso Robles Formation</div> <div>3 - Gray Member, Paso Robles Formation</div> <div>4 - Undifferentiated Paso Robles Formation</div> <div>5 - Quaternary alluvium (older)</div> <div>6 - Quaternary alluvium (younger)</div> <div>7-8 - Quaternary landslides and terraces</div> <div> </div> <div>(iii) Cumulative uplift. We follow the general approach of Hilley & Arrowsmith (2008) to reconstruct the cumulative uplift at Dragon’s Back Pressure Ridge based on the observed positions and elevations of the stratigraphic contacts between the Pink, Tan, and Gray members of the Paso Robles Formation. Put simply, since the Pink, Tan, and Gray members of the Paso Robles Formation are initially flat-lying, the progressive increase in elevation of the contacts between these members from the start to the middle of the Dragon’s Back Pressure Ridge records the cumulative tectonic uplift. We perform a Markov Chain Monte Carlo (MCMC) inversion to reconstruct the uplift history that can best explain our geological observations (i.e., the positions and elevations of the Pink, Tan, and Gray members of the Paso Robles Formation). See section 6 for the Matlab code used to perform the MCMC inversion.</div> <div>Dataset A: “cumulative_uplift_mean.tif” -- the mean reconstructed cumulative uplift (units: meters).</div> <div>Dataset B: “cumulative_uplift_uncertainty_IQR.tif” -- the uncertainty of the reconstructed cumulative uplift (units: meters), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC cumulative uplift estimates.</div> <div> </div> <div>(iv) Cumulative uplift rate. The uplift rate dataset is constructed by taking the spatial derivative of the cumulative uplift dataset (iii) in the along-strike direction of the San Andreas Fault, and then converting from space to time using the long-term slip rate on the San Andreas Fault of approximately 33 mm/yr. This is the same approach as used in Hilley & Arrowsmith (2008).</div> <div>Dataset A: “uplift_rate_mean.tif” -- the mean reconstructed uplift rate (units: mm/yr).</div> <div>Dataset B: “uplift_rate_uncertainty_IQR.tif” -- the uncertainty of the reconstructed uplift rate (units: mm/yr), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC estimates.</div> <div> </div> <div> </div> <div>2. Ground penetrating radar (GPR).</div> <div> </div> <div> <p>The GPR data were acquired with a MALA HDR GPR system with a 450 MHz shielded antenna. Data were acquired every 4 cm, tracked by a survey wheel for precise relative positioning. The GPR survey covered approximately 19 km of ridgeline and included 21 short (approximately 10 m) ridgetop profiles with cone penetrometer observations that serve as ground-truth for the depth of the soil-saprolite boundary inferred from the GPR data. The GPR data were processed using the open-source GPRPy software (Plattner, 2020). We constrained sub-surface velocities by fitting 364 diffraction hyperbolas in the GPR transects. The hyperbola fitting supports a spatially-uniform velocity of approximately 0.11 m/ns. The locations and fitted velocities of the individual hyperbolas used to construct this velocity model are included in the file “GPR_velocities.csv.”</p> <p>The folder “Radar450MHz_raw” includes the raw radar data. The folder “Radar450MHz_GPS” includes the GPS data associated with each radar dataset (saved as .cor files). The GPS observations are aggregated in the spreadsheet “GPS_all” in that folder. The shapefile folder includes the final processed GPR-derived soil thickness estimates (soil thickness reported in units of meters) as a .shp file. The coordinate system for the shapefile is WGS84 / UTM Zone 11 N. </p> </div> <div> </div> <div>3. Geomorphic properties.</div> <div> </div> <div>These are the datasets plotted in Figures 3 and 4 of “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>4. Cone penetrometer observations.</div> <div> </div> <div> <div>This folder contains 3 files:</div> <div>1. ConePenetrometerSummaryTable_Overview.csv: A summary of the 212 cone penetrometer stations. For each station, there is metadata about the location (GPS coordinates), the stratigraphic unit (Pink, Tan, or Gray Member of the Paso Robles Formation), the side of the ridge, (southeast = SW, center = C, or northwest = NW), and the saprolite weakness, calculated as the cone penetrometer ease of penetration [cm/strike] at the position of the soil-saprolite boundary. </div> <div>2. ConePenetrometerSummaryTable_Data.csv: All of the raw observations from the cone penetrometer. The raw observations are the cumulative strike number vs. the cumulative depth of penetration into the ground.</div> <div>3. ConePenetrometerSummaryTableFinal.xlsx: An Excel spreadsheet with the same data from items (1) and (2) above as separate sheets ("Overview") and ("Data").</div> </div> <div> </div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>This folder contains 2 files:</div> <div>1. soil_pit_summary: A summary table containing the soil pit locations and the inferred depth to the soil-saprolite boundary.</div> <div>2. soil_pit_layers: Simplified stratigraphic columns providing grain sizes and classifications (soil vs. saprolite) of the layers identified in each soil pit.</div> <div> </div> <div> </div> <div>6. Matlab code. </div> <div> </div> <div>This folder contains 2 primary Matlab scripts, with supporting data files and helper functions.</div> <div>1. DBPR_uplift: code to reconstruct the tectonic uplift at DBPR based on the positions and elevations of the stratigraphic contacts.</div> <div>1. soil_depth_vs_strength: code to reconstruct Fig. 4 Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>References</strong></div> <div> </div> <div>Arrowsmith, J. R. Coupled tectonic deformation and geomorphic degradation along the San Andreas Fault System. Ph.D. thesis, Stanford University (1995).</div> <div> </div> <div>Dibblee Jr, T. Regional geologic map of San Andreas Fault and related faults and Carrizo Plain, Temblor, Caliente, and La Panza Ranges and vicinity, California. US Geological Survey Miscellaneous Geological Investigations, Map I-757, scale 1:125,000 (1973). </div> <div> </div> <div>Dibblee, T. W. et al. Regional geologic map of San Andreas and related faults in Carrizo Plain, Temblor, Caliente and La Panza Ranges and vicinity, California: A digital database. Tech. Rep., US Geological Survey (1999). </div> <div> </div> <div>Hilley, G. E. & Arrowsmith, J. R. Geomorphic response to uplift along the Dragon’s Back pressure ridge, Carrizo Plain, California. Geology, 36, 367–370 (2008). </div> <div> </div> <div>Plattner, A. M. GPRPy: Open-source ground-penetrating radar processing and visualization software. The Leading Edge 39, 332–337 (2020).</div> <div> </div>
Soil resources mediate the strength of species but not trait convergence across grassland restorations
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Data from: Fire effects on soil biota alter the strength and direction of plant-soil feedbacks between Schizachyrium scoparium (Michx.) Nash and Rudbeckia hirta L.
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Moisture alone is sufficient to impart strength but not weathering resistance to termite mound soil
<p>Soil is used for construction of structures by many animals, at times admixed with endogenous secretions. These additives, along with soil components, are suggested to have a role in biocementation. However, the relative contribution of endogenous and exogenous materials to soil strength has not been adequately established. Termite mounds are earthen structures with exceptional strength and durability including weathering resistance to wind and rain. With <i>in-situ</i> and lab-based experiments we demonstrate that the fungus-farming termite <i>Odontotermes obesus</i> which builds soil nest mounds, when given a choice, prefers soil close to its liquid limit for construction. At this moisture content, the soil–water mixture alone even in the absence of termite handling undergoes self-weight consolidation and upon drying attains a monolithic, densely packed structure with compressive strength comparable to the <i>in-situ</i> strength of the mound soil; however, the soil–water mixture alone has lower resistance to water erosion than the <i>in-situ</i> mound samples suggesting that termite secretions impart weathering resistance and thereby long-term stability to the mound. Therefore, weathering resistance and compressive strength are conferred by different aspects of termite soil manipulation. Our work provides novel insights into termite mound construction and strength correlates for earthen structures built by animals.</p>
Even short-term revegetation complicates soil food webs and strengths their links with ecosystem functions
<p>Degradation of dryland ecosystems is a worldwide problem caused by climate change and human activities. To restore these degraded ecosystems, governments have implemented projects that often include revegetation, but we still lack an understanding of how soil food webs and ecosystem functions are affected by revegetation. By conducting a large-scale revegetation experiment under two degradation intensities (low and high) on the Inner Mongolian degraded grassland, we tested the effects of revegetation on primary producers (plants), key components of soil food webs (bacteria, fungi, and nematodes), and ecosystem functions (soil C and N mineralization). After 4 years, revegetation greatly increased the biomass of plants and soil bacteria and fungi but had less effects on soil nematode functional groups. Revegetation increased vegetation and bacterial diversities and soil C and N mineralization rates, altered the structures of vegetation and soil microbial communities, but did not affect fungal or nematode diversity. The stronger effects of revegetation on plants, soil bacteria, soil fungi, and soil nematodes in plots with low degradation intensity than in plots with high degradation intensity indicated that future revegetation efforts should consider the degree of degradation. Revegetation also increased the interactions among plants, soil food webs, and ecosystem functions, indicating that the revegetation-induced changes in soil food webs could facilitate the recovery of soil nutrients and vegetation productivity in degraded grasslands. Synthesis and applications. Overall, the effects of revegetation were stronger on plants (primary producers) and soil microorganisms (intermediate trophic levels) than on soil nematodes (higher trophic levels), and even short-term revegetation increased the complexity of soil food webs and strengthened their relationships with soil functions in degraded grasslands. These results highlight the effects of restoration on multiple trophic levels in degraded drylands, and suggest that some aspects of plant-soil interactions in global drylands could be rapidly improved by appropriate restoration.</p>
Moisture alone is sufficient to impart strength but not weathering resistance to termite mound soil
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Even short-term revegetation complicates soil food webs and strengths their links with ecosystem functions
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Data from: Use of elastic stability analysis to explain the stress-dependent nature of soil strength
The peak and critical state strengths of sands are linearly related to the stress level, just as the frictional resistance to sliding along an interface is related to the normal force. The analogy with frictional sliding has led to the use of a 'friction angle' to describe the relationship between strength and stress for soils. The term 'friction angle' implies that the underlying mechanism is frictional resistance at the particle contacts. However, experiments and discrete element simulations indicate that the material friction angle is not simply related to the friction angle at the particle contacts. Experiments and particle-scale simulations of model sands have also revealed the presence of strong force chains, aligned with the major principal stress. Buckling of these strong force chains has been proposed as an alternative to the frictional-sliding failure mechanism. Here, using an idealized abstraction of a strong force chain, the resistance is shown to be linearly proportional to the magnitude of the lateral forces supporting the force chain. Considering a triaxial stress state, and drawing an analogy between the lateral forces and the confining pressure in a triaxial test, a linear relationship between stress level and strength is seen to emerge from the failure-by-buckling hypothesis.
Data from: The strength of negative plant-soil feedback increases from the intraspecific to the interspecific and the functional group level
1. One of the processes that may play a key role in plant species coexistence and ecosystem functioning is plant-soil feedback, the effect of plants on associated soil communities and the resulting feedback on plant performance. Plant-soil feedback at the interspecific level (comparing growth on own soil with growth on soil from different species) has been studied extensively, while plant-soil feedback at the intraspecific level (comparing growth on own soil with growth on soil from different accessions within a species) has only recently gained attention. Very few studies have investigated the direction and strength of feedback among different taxonomic levels, and initial results have been inconclusive, discussing phylogeny and morphology as possible determinants. 2. To test our hypotheses that the strength of negative feedback on plant performance increases with increasing taxonomic level and that this relationship is explained by morphological similarities, we conducted a greenhouse experiment using species assigned to three taxonomic levels (intraspecific, interspecific and functional group level). We measured certain fitness-related aboveground traits and used them along literature-derived traits to determine the influence of morphological similarities on the strength and direction of the feedback. 3. We found that the average strength of negative feedback increased from the intraspecific over the interspecific to the functional group level. However, individual accessions and species differed in the direction and strength of the feedback. None of our results could be explained by morphological dissimilarities or individual traits. 4. Synthesis. Our results indicate that negative plant-soil feedback is stronger if the involved plants belong to more distantly related species. We conclude that the taxonomic level is an important factor in the maintenance of plant coexistence with plant-soil feedback as a potential stabilizing mechanism and should be addressed explicitly in coexistence research, while the traits considered here seem to play a minor role.
Data from: Use of elastic stability analysis to explain the stress-dependent nature of soil strength
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Data from: The strength of negative plant-soil feedback increases from the intraspecific to the interspecific and the functional group level
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Dataset: Effects of flooding and nutrients on Spartina patens and Sagittaria lancifolia soil shear strength: a greenhouse study
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