Publications by Jonathan Spiess
JEM forage graphs
#Nutritive Values ##Biomass TSF and interaction significant Overall TSF: RB < others, 3yr > intermediate and NYB Interaction: Cattle: RB < others; Sheep: RB < others, Intermediate & 3yr > NYB ## Analysis of Deviance Table (Type II Wald chisquare tests) ## ## Response: log(KgHa + 1) ## Chisq Df Pr(>Chisq) ## TSF 4...
1148 sym 15 img
SRM 2024 Workbook
Floral Abundance and Richness RichCountLM <- lmer(FloralCountMean ~ FloralRichnessMean + (1|Plot), data=SRFsummary2, REML = FALSE) summary(RichCountLM) ## Linear mixed model fit by maximum likelihood ['lmerMod'] ## Formula: FloralCountMean ~ FloralRichnessMean + (1 | Plot) ## Data: SRFsummary2 ## ## AIC BIC logLik deviance df....
44 sym 2 img
PBG Microbe Ordinations from SRM Poster
Measured variables were either higher in recently burned patches (RB) or were not different from patches that had previously been burned (1YSB, 2YSB) or had not yet been burned (NYB). Total nitrogen, total carbon, potassium, litter bag decomposition, microbial biomass, and soil moisture were here higher in the recentl burned patches than the unbu...
664 sym 3 img
Chapter 2 Workbook
Biomass Cattle Fecal Count ## Warning: Removed 66 rows containing non-finite values (stat_boxplot). ## Warning: Removed 4 rows containing missing values (geom_point). ## Warning: Removed 4 rows containing missing values (geom_linerange). Sheep Fecal Count Crude Protein ## Warning: Removed 7 rows containing non-finite values (stat_boxplot). #...
117 sym R (1036 sym/15 pcs) 16 img
Soils SRM 2021
Hey! Here is what I am looking at doing with the soil nutrients and microbe data for SRM. Including Plots You can also embed plots, for example: Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot. ...
278 sym 2 img
Soils Chapter 1 Scratchpad
Overview I got around to graphing the soil nutrient data from the individual patch perspective! The monthly meaurements make a for messier figures than the July only variables, so I graphed those a little differently. The July only varibles make a nice grid by location (pasture). One of the cooler things I noticed on this round of graphs is that ...
652 sym R (828 sym/12 pcs) 25 img
Soil Ordi 2019 and 2020
Microbes! Ordination for both years and determining which variables are significant Mic_canb1920 <- capscale(MicBiomass1920 ~ 1, metaMDSdist = "true", dist="canb") ## Square root transformation ## Wisconsin double standardization summary(Mic_canb1920) ## ## Call: ## capscale(formula = MicBiomass1920 ~ 1, distance = "canb", metaMDSdist = "true...
856 sym R (67976 sym/35 pcs) 8 img
Veg Summary Graphs
Overview We did some fire and grazing to try to get patch contrast in veg structure…so what happened?! While precip, season of fire, and timing of grazing influence these reults, we have pretty consistent differences between the recently burned patches and the unburned/3yr+ patches across most variables. 2019 is not great for contrast thanks to...
1086 sym 10 img
Soil Models Workbook
SoilTukey21 <-read.csv("D:/R/data/SRMSoils 2021Tukey.csv", head=TRUE, stringsAsFactors = FALSE) SoilTukey21$Variable <- factor(SoilTukey21$Variable) SoilTukey21$Contrast <- factor(SoilTukey21$Contrast) SoilTukey21$Years <- factor(SoilTukey21$Years) print(levels(SoilTukey21$Variable)) ## [1] "Ammonium" "Calcium" "Decomposition...
443 sym R (119057 sym/223 pcs) 52 img
NIR Ordination Workbook
HRECSICalc <- HRECSICalc %>% mutate(TDNg= 98.625-(1.048*ADF)) %>% mutate(TDNm= 92.62-(0.9093*ADF)) %>% mutate(TDNc=82.14-(0.577*ADF)) NIRpatches <- HRECSICalc %>% gather(Moisture,Powell:CP, TDNg, TDNm, TDNc, key="species", value="cover") %>% mutate(cover=as.numeric(cover)) %>% group_by(Year, TSF, Treatment, Location, Patch, Mo...
244 sym R (90126 sym/29 pcs) 8 img