lakeWA: Lake Washington Plankton Data

In nwfsc-timeseries/atsalibrary: Packages, data and scripts for ATSA course and lab book

Lake Washington Plankton Data

Description

Monthly Lake Washington (WA, USA) plankton, temperature, total phosphorous, and pH data 1962 to 1994.

Usage

data(lakeWA)

Format

Object of class "data.frame". #'

Details

The lakeWA is a 32-year time series (1962-1994) of monthly plankton counts from Lake Washington, Washington, USA. lakeWA is a transformed version of the raw data (available in the MARSS package data(lakeWAplanktonRaw, package="MARSS")). Zeros have been replaced with NAs (missing). The plankton counts are logged (natural log) and standardized to a mean of zero and variance of 1 (so logged and then z-scored). Temperature, TP & pH were also z-scored but not logged (so z-score of the untransformed values for these covariates). The single missing temperature value was replaced with -1 and the single missing TP value was replaced with -0.3. The two missing pH values were interpolated. Monthly anomalies for temperature, TP and pH were computed by removing the monthly means (computed over the 1962-1994 period). The anomalies were then z-scored to remove mean and standardize variance to 1.

References

Adapted from the Lake Washington database of Dr. W. T. Edmondson, as funded by the Andrew Mellon Foundation; data courtesy of Dr. Daniel Schindler, University of Washington, Seattle, WA.

Hampton, S. E. Scheuerell, M. D. Schindler, D. E. (2006) Coalescence in the Lake Washington story: Interaction strengths in a planktonic food web. Limnology and Oceanography, 51, 2042-2051.

Examples

# The lakeWA data frame was created with the following code:
## Not run: 
data(lakeWAplankton, package = "MARSS")
lakeWA <- data.frame(lakeWAplanktonTrans)
# add on month and date columns
lakeWA$Month.abb <- month.abb[lakeWA$Month]
lakeWA$Date <- as.Date(paste0(lakeWA$Year, "-", lakeWA$Month,"-01"))
# interpolate 2 missing values in pH
lakeWA$pH[is.na(lakeWA$pH)] <- MARSS::MARSS(lakeWA$pH)$states[1, is.na(lakeWA$pH)]
# create monthly anomalies
lakeWA$Temp.anom <- residuals(lm(Temp ~ Month.abb, data=lakeWA))
lakeWA$TP.anom <- residuals(lm(TP ~ Month.abb, data=lakeWA))
lakeWA$pH.anom <- residuals(lm(pH ~ Month.abb, data=lakeWA))
# resort the columns
lakeWA <- lakeWA[,c(22,1:2,21,3:5,23:25,6:20)]
# zscore everything
for (i in 5:25) lakeWA[[i]] <- MARSS::zscore(lakeWA[[i]])
save( lakeWA, file="data/lakeWA.RData" )

## End(Not run)

library(ggplot2)
library(tidyr)
df <- lakeWA %>% 
     pivot_longer(
         cols = Temp:Non.colonial.rotifers,
         names_to = "variable",
         values_to = "value"
         )
ggplot(df, aes(x=Date, y=value)) + 
     geom_line() + 
     facet_wrap(~variable)   

data(lakeWA)

nwfsc-timeseries/atsalibrary documentation built on May 11, 2023, 2:25 a.m.