Mapping Soilscapes Using Soil Co-Occurrence...
Transcript of Mapping Soilscapes Using Soil Co-Occurrence...
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Mapping Soilscapes Using Soil Co-Occurrence Networks
D.E. Beaudette USDA-NRCS Soil and Plant Science Div.P. Roudier Manaaki Whenua Landcare Research
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Acknowledgments• Pierre Roudier and Dion O’Neale• A.T. O’Geen, C. Stiles, Drew Kinney• 100+ years of soil survey effort• R graph libraries (igraph, ape)• R soil science related libraries (soilDB, sharpshootR)
This talk is about quantifying co-occurrence within tabular data
• 1977, 1985: F.D. Hole → soilscapes defines patterns of association• 2001: P. Lagacherie et al. → soilscapes as landscape signature• 2010: A.E. Hewitt et al. → soilscapes as modeling domains • 2013/2016: J.D. Phillips → spatial adjacency of mapping units• 2013—current: SoilWeb Series Data Explorer• 2014/2017: D.E. Beaudette and P. Roudier → mapping co-occurrence• 2018/2019: Odgers, Roudier, Thompson, Beaudette → soilscapes in NZ/US
Prior Work
This talk is not about quantifying spatial co-occurrence
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Graph / Network Concepts
• node or vertex: soil series• edge weight: magnitude of association• adjacency: quantification of association
• co-occurrence (cross-tabulate)• distance (distance matrix)• similarity (restatement of distance)
• degree: number of edges / node• community detection: clustering patterns of co-occurrence
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Figure by Andrew Brown, USDA-NRCS, Sonora, CA
Soilscapes → Mapping Units → Soil Series
Lithic Haploxeroll Humic Haploxerand
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Soilscapes → Mapping Units → Soil Series
Lloyd-Davidson association (Soil Survey of Morgan County, Georgia; 1965)
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Soilscapes → Mapping Units → Soil Series
Lloyd-Davidson association (Soil Survey of Morgan County, Georgia; 1965)
top bottom
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Figure by Andrew Brown, USDA-NRCS, Son
Ultic Haploxeralfs
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Figure by Andrew Brown, USDA-NRCS, Son
Ultic Haploxeralfs, complex arrangement
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Mapping Units in SSURGO
mukey compname comppct_r-------- ------------ ----------1612048 Amador 451612048 Gillender 401612048 Ranchoseco 31612048 Vleck 31612048 Pardee 31612048 Peters 3
2766838 Amador 762766838 Gillender 92766838 Pardee 52766838 Miltonhills 52766838 Redding 2
...
complex
consociation
Amador: Loamy, mixed, superactive, thermic, shallow Typic Haploxerepts
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Mapping Units in SSURGOmukey compname comppct_r-------- ------------ ----------1612048 Amador 451612048 Gillender 40
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Mapping Units in SSURGOmukey compname comppct_r-------- ------------ ----------2766838 Amador 762766838 Gillender 92766838 Pardee 52766838 Miltonhills 52766838 Redding 2
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“Siblings”Components* that co-occur with Amador in map units
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“Siblings”Components* that co-occur with Amador in map units
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“Siblings”Components* that co-occur with Amador in map units
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Quantify Co-Occurrence: Adjacency Matrix
Gillender Miltonhills Pardee Peters Ranchoseco Redding Vleck------------ ---------- ------------ ------- ------- ----------- -------- ------Amador 2 1 2 1 1 1 1Gillender 1 2 1 1 1 1Miltonhills 1 1 Pardee 1 1 1 1Peters 1 1Ranchoseco 1Redding Vleck
Tabulate “co-occurrence” → information is lost
Compute “similarity”
1. collect mapunit / component records2. reshape into "community matrix"3. standardize and compute distance matrix (methods from numerical ecology)4. convert distance matrix into similarity matrix
→ this is the adjacency matrix (details on next slide)
Why all the fuss? Component percentages (weights) matter!
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461845 461980 462528 462529 462530 462531 462954 462955------------ ------- ------- ------- ------- ------- ------- ------- -------Amador 45 25 85 85 85 85 85 85Corning 0 5 0 0 0 0 0 0Exchequer 0 0 5 5 5 5 0 0Gillender 40 0 0 0 0 0 0 0Hornitos 0 0 5 5 5 5 10 10Inks 0 0 0 0 0 0 0 0Miltonhills 0 0 0 0 0 0 0 0Pardee 3 0 0 0 0 0 0 0Pentz 0 0 5 5 5 5 5 5Peters 3 0 0 0 0 0 0 0Ranchoseco 3 0 0 0 0 0 0 0Redding 0 4 0 0 0 0 0 0Vleck 3 40 0 0 0 0 0 0
Pseudo-community Matrix
Gillender Miltonhills Pardee Peters Ranchoseco Redding Vleck------------ ---------- ------------ ------- ------- ----------- -------- ------Amador 0.5 0.03 0.07 0.03 0.03 0.01 0.03Gillender 0.07 0.13 0.07 0.07 0.03 0.07Miltonhills 0.50 0.40 Pardee 0.50 0.50 0.20 0.50Peters 1.00 1.00Ranchoseco 1.00Redding Vleck
Similarity Matrix (via Jaccard index)
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Adjacency Matrix → GraphGillender Miltonhills Pardee Peters Ranchoseco Redding Vleck
------------ ---------- ------------ ------- ------- ----------- -------- ------Amador 0.5 0.03 0.07 0.03 0.03 0.01 0.03Gillender 0.07 0.13 0.07 0.07 0.03 0.07Miltonhills 0.50 0.40 Pardee 0.50 0.50 0.20 0.50Peters 1.00 1.00Ranchoseco 1.00Redding Vleck
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Why are we doing this? https://xkcd.com/1838/
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Why are we doing this?Explicit, quantitative, human/machine readable encodingof historic (and future) soil knowledge.
Continuous predictions of soil properties / membership are(of course) important.
Failing to integrate hard-won knowledge(collected and synthesized in places where soils occur)into modern efforts would be a terrible tragedy.
Groups of co-occurring soils are useful strata that should be integrated into statistical models.
Co-occurrence networks are neat.
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Thank You
Generate Soil Networks in Rhttp://ncss-tech.github.io/AQP/https://goo.gl/6HMWRR
soilDB::siblings()soilDB::fetchOSD()sharpshootR::component.adj.matrix()sharpshootR::polygonAgacency()
USDA is an equal opportunity employer, provider and lender
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Adjacency Matrix → GraphGillender Miltonhills Pardee Peters Ranchoseco Redding Vleck
------------ ---------- ------------ ------- ------- ----------- -------- ------Amador 2 1 2 1 1 1 1Gillender 1 2 1 1 1 1Miltonhills 1 1 Pardee 1 1 1 1Peters 1 1Ranchoseco 1Redding Vleck