SCOC: a Sediment Community Oxygen Consumption (SCOC) dataset

In ecolMod: "A Practical Guide to Ecological Modelling - Using R as a Simulation Platform"

a Sediment Community Oxygen Consumption (SCOC) dataset

Description

This literature dataset, compiled by Andersson et al. (2004) contains 584 measurements of sediment community oxygen consumption rates, as a function of water depth, and performed in deep-water sediments, either by in situ incubations or via modelling of oxygen microprofiles.

It is used in the book to demonstrate how one can obtain order-of-magnitude estimates of model parameters (i.c. sediment oxygen consumption rate, a measure of deposition flux) by performing log-log regression with water depth.

Usage

SCOC

Format

a dataframe with 584 rows, and with following columns:
Depth.m, the water depth at which the measurement was performed.
SCOC.mmol/m2/d, the oxygen consumption rate of the sediment, [mmolO2/m2/d]

Author(s)

Karline Soetaert <[e-mail]>

References

Andersson, H., Wijsman, J., Herman, P., Middelburg, J., Soetaert, K., Heip, C., 2004. Respiration patterns in the deep ocean. Geophysical Research Letters 31, LO3304.

See Also

Zoogrowth, a dataset containing zooplankton maximal growth rates
see the paper of Andersson et al. for a description of the original literature sources of this dataset

Examples

plot(SCOC[,1], SCOC[,2], log = "xy", xlab = "water depth, m",
     ylab = "", main = "SCOC, mmol O2/m2/d", pch = 16,
     xaxt = "n", yaxt = "n", cex.main = 1)

axis(1, at = c(0.5, 5, 50, 500, 5000), 
     labels = c("0.5", "5", "50", "500", "5000"))
axis(2, at = c(0.1, 1, 10, 100), 
     labels = c("0.1", "1", "10", "100"))

ll <- lm(log(SCOC[,2])~ log(SCOC[,1]))
rr <- summary(ll)$r.squared
A  <- exp(coef(ll)[1])
B  <- (coef(ll)[2])
curve(A*x^B, add = TRUE, lwd = 2)
AA <- round(A*100)/100
BB <- round(B*100)/100
expr <- substitute(y==A*x^B, list(A=AA,B=BB))
text(1, .1, expr, adj = 0)
expr2 <- substitute(r^2==rr, list(rr=round(rr*100)/100))
text(1, 0.04, expr2, adj = 0)

ecolMod documentation built on Nov. 16, 2022, 1:06 a.m.