spe.chi$Satr,
type = "l",
col = 4,
main = "Chi-square-transformed abundances",
xlab = "Distance from the source [km]",
ylab = "Standardized abundance"
)
lines(env$dfs, spe.chi$Thth, col = 3)
lines(env$dfs, spe.chi$Baba, col = "orange")
lines(env$dfs, spe.chi$Abbr, col = 2)
lines(env$dfs, spe.chi$Babl, col = 1, lty = "dotted")
legend("topright",
c("Brown trout", "Grayling", "Barbel", "Common bream",
"Stone loach"),
col = c(4, 3, "orange", 2, 1),
lty = c(rep(1, 4), 3)
)
Compare the graphs and explain the differences.
In some cases (often vegetation studies), data are collected using abundance
scales that are meant to represent specific properties: number of individuals (abundance classes), cover (dominance classes), or both (e.g. Braun-Blanquet abundancedominance scale). The scales being ordinal and somewhat arbitrary, the resulting
data do not easily lend themselves to a simple transformation. In such cases one may
have to convert scales by attributing values according to the data at hand. For
discrete scales it can be done by function vegtrans() of package labdsv.
For example, assuming we knew how to convert the fish abundance codes
(ranging from 0 to 5 in our spe dataset) to average numbers of individuals, we
could do it by providing two vectors, one with the current scale and one with the
converted scale. Beware: this would not make sense for this fish data set, whose
abundances are species-specific (see Sect. 2.2).
## Conversion of the fish abundance using an arbitrary scale
current <- c(0, 1, 2, 3, 4, 5)
converted <- c(0, 1 ,5, 10, 20, 50)
spe.conv <- vegtrans(spe, current, converted)
2.2 Data Exploration
27
type = "l",
col = 4,
main = "Chi-square-transformed abundances",
xlab = "Distance from the source [km]",
ylab = "Standardized abundance"
)
lines(env$dfs, spe.chi$Thth, col = 3)
lines(env$dfs, spe.chi$Baba, col = "orange")
lines(env$dfs, spe.chi$Abbr, col = 2)
lines(env$dfs, spe.chi$Babl, col = 1, lty = "dotted")
legend("topright",
c("Brown trout", "Grayling", "Barbel", "Common bream",
"Stone loach"),
col = c(4, 3, "orange", 2, 1),
lty = c(rep(1, 4), 3)
)
Compare the graphs and explain the differences.
In some cases (often vegetation studies), data are collected using abundance
scales that are meant to represent specific properties: number of individuals (abundance classes), cover (dominance classes), or both (e.g. Braun-Blanquet abundancedominance scale). The scales being ordinal and somewhat arbitrary, the resulting
data do not easily lend themselves to a simple transformation. In such cases one may
have to convert scales by attributing values according to the data at hand. For
discrete scales it can be done by function vegtrans() of package labdsv.
For example, assuming we knew how to convert the fish abundance codes
(ranging from 0 to 5 in our spe dataset) to average numbers of individuals, we
could do it by providing two vectors, one with the current scale and one with the
converted scale. Beware: this would not make sense for this fish data set, whose
abundances are species-specific (see Sect. 2.2).
## Conversion of the fish abundance using an arbitrary scale
current <- c(0, 1, 2, 3, 4, 5)
converted <- c(0, 1 ,5, 10, 20, 50)
spe.conv <- vegtrans(spe, current, converted)
2.2 Data Exploration
27