PhytoIn: Tools for vegetation analysis and forest inventory

Description

The package provides functions and datasets for phytosociological analysis, forest inventory, biomass/carbon estimation, volume stratification, and visualization (see phytoparam, AGB, stratvol, BAplot, plot.param, collector.curve, rarefaction, stats, summary.param).

Datasets

• quadrat.df

• point.df

• quadrat2_plot.df

• quadrat2_tree.df

• quadrat3_rect.df

Author(s)

Maintainer: Rodrigo Augusto Santinelo Pereira raspereira@usp.br

See Also

Useful links:

• https://github.com/PhytoIn/PhytoIn

• Report bugs at https://github.com/PhytoIn/PhytoIn/issues

Estimate the above-ground biomass

Description

Estimate the above-ground biomass (AGB), carbon (C) and CO_2 equivalent (CO_2e) of trees.

Usage

AGB(
  x,
  measure.label,
  h,
  taxon = "taxon",
  dead = "dead",
  circumference = TRUE,
  su = "quadrat",
  area,
  coord,
  rm.dead = TRUE,
  check.spelling = FALSE,
  correct.taxon = TRUE,
  sort = TRUE,
  decreasing = TRUE,
  cache = FALSE,
  long = FALSE
)

Arguments

Details

AGB is a wrapper around BIOMASS functions getWoodDensity, computeAGB, and correctTaxo (Rejou-Mechain et al., 2017). Tree biomasses are computed using the allometric model of Chave et al. (2014).

It is expected that taxon names are binomials (genus and species). The function splits the taxon string into two columns (genus, species) to retrieve wood density. Single-word taxa (e.g., Indet, Dead) receive species = NA.

Wood density (g/cm^3) is obtained from a global database (~16,500 species). If a species is missing, the genus mean is used; if the genus is missing, the sample-unit (su) mean is used.

The input x must include columns for sample-unit labels, taxon names, CBH/DBH, and optionally height. If height is absent, coord is mandatory to allow height estimation.

The CBH/DBH column allows multi-stem notation such as "17.1+8+5.7+6.8". The plus sign separates stems; decimal separators can be points or commas; spaces around "+" are ignored. Column names in x are coerced to lower case at runtime, making the function case-insensitive.

Measurement units: CBH/DBH in centimeters; height in meters.

Value

An object of class "biomass" with up to four components:

• $tree: individual records (taxon, wood density wd, assignment level, AGB, C, CO2e);

• $taxon: AGB, C, and CO_2e per taxon (Mg per ha);

• $total: total AGB, C, and CO_2e per ha;

• $WD.level: percentage of wood-density assignments at species, genus, and sample-unit levels.

References

Boyle, B. et al. (2013) BMC Bioinformatics 14:16.
Chave, J. et al. (2014) Global Change Biology 20(10):3177–3190.
Rejou-Mechain, M. et al. (2017) Methods in Ecology and Evolution 8:1163–1167.
Zanne, A.E. et al. (2009) Global wood density database. Dryad.

See Also

BIOMASS (getWoodDensity, computeAGB, correctTaxo)

Examples

data <- quadrat.df
head(data)

resul1 <- AGB(
  data, measure.label = "CBH", h = "h", taxon = "Species", dead = "Morta",
  circumference = TRUE, su = "Plot", area = 0.0625, rm.dead = TRUE,
  check.spelling = FALSE, correct.taxon = TRUE, sort = TRUE,
  decreasing = TRUE, long = TRUE
)
head(resul1$tree)
resul1$taxon
resul1$total
resul1$WD.level

quadrat.default <- quadrat.df
colnames(quadrat.default) <- c("quadrat", "family", "taxon", "cbh", "h")

Resul2 <- AGB(x = quadrat.default, measure.label = "cbh",
              circumference = TRUE, h = "h", dead = "Morta", area = 0.0625)
head(Resul2$tree)
Resul2$taxon
Resul2$total
Resul2$WD.level
## Not run: 
Resul3 <- AGB(data, measure.label = "CBH", taxon = "Species", dead = "Morta",
              circumference = TRUE, su = "Plot", area = 0.0625,
              coord = c(-47.85, -21.17))
Resul3$taxon
Resul3$total
Resul3$WD.level

## End(Not run)

Plot basal areas on a map of quadrats

Description

Plot basal areas of trees on a map of quadrats. If individual tree coordinates are not known, the coordinates inside the quadrats are randomly defined according to the uniform distribution.

Usage

BAplot(
  formula,
  data,
  taxon = "taxon",
  circumference = TRUE,
  quadrat.size,
  dead = "dead",
  rm.dead = FALSE,
  origin = c(0, 0),
  col = "grey40",
  alpha = 1,
  cex.radius = 1,
  ind.coord = FALSE,
  legend = TRUE,
  long = FALSE
)

Arguments

Details

BAplot uses the function circleRepelLayout() from the packcircles package to rearrange circle coordinates to avoid overlapping. The minimum distance allowed among trees is 1 meter. The packages ggforce and ggplot2 are used to draw the map.

The data frame passed to the data argument must include two columns indicating x and y coordinates of each quadrat that the tree belongs or the actual tree coordinates. If actual coordinates are supplied, the ind.coord argument must be set TRUE.

Circumference/diameter measures accept the traditional notation for multiple trunks, e.g., "17.1+8+5.7+6.8". The plus sign is the separator for each trunk measure. Decimal separator can be point or comma and spaces after or before "+" are ignored by the function.

Value

A plot representing the quadrat map and tree basal areas. If long = TRUE, the function returns a data frame containing taxon name, xy coordinates and radius of each sampled tree.

Author(s)

Rodrigo A. S. Pereira (raspereira@usp.br)

References

Collins, C. R., and Stephenson, K. (2003). A circle packing algorithm. Computational Geometry, 25(3), 233–256. doi:10.1016/S0925-7721(02)00099-8

Wang, W., Wang, H., Dai, G., and Wang, H. (2006). Visualization of large hierarchical data by circle packing. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 517–520. doi:10.1145/1124772.1124851

Examples

# Using plot coordinates (random coordinates for individuals)
data1 <- quadrat2_plot.df
BAplot(formula = CBH ~ x + y, data = data1, taxon = "Species",
       circumference = TRUE, quadrat.size = 5, dead = "Morta",
       rm.dead = FALSE, alpha = 0.4, cex.radius = 2,
       legend = TRUE, long = FALSE, ind.coord = FALSE)

# Using actual coordinates
data2 <- quadrat2_tree.df
BAplot(formula = CBH ~ x + y, data = data2, taxon = "Species",
       circumference = TRUE, quadrat.size = 5, dead = "Morta",
       rm.dead = FALSE, alpha = 0.4, cex.radius = 2,
       legend = TRUE, long = FALSE, ind.coord = TRUE)

# Rectangular plots and plot coordinates
data3 <- quadrat3_rect.df
BAplot(formula = DBH ~ x + y, data = data3, taxon = "Species",
       circumference = FALSE, quadrat.size = c(20, 10),
       dead = "Morta", rm.dead = FALSE, col = "blue",
       alpha = 0.4, cex.radius = 2, legend = FALSE,
       long = FALSE, ind.coord = FALSE)

Species–area (collector's curve) function

Description

Computes species accumulation (collector's) curves based on sample units (SUs). The function performs random resampling of the input matrix or data frame to estimate the expected species richness per number of SUs, with confidence intervals derived from multiple permutations.

Usage

collector.curve(
  formula,
  data,
  x,
  times = 1000,
  replace = FALSE,
  prob = 0.95,
  spar = 0,
  xlab,
  ylab,
  plot = TRUE,
  long = FALSE,
  theme = "theme_classic"
)

Arguments

Details

Species accumulation curves are computed by sequentially adding sample units and recording species richness across permutations. Confidence intervals are estimated from the empirical distribution of resampled richness values. The plotted confidence intervals are smoothed using spline interpolation if spar > 0.

It is recommended to assign the output to an object, as the complete output (particularly with long = TRUE) can be large.

Value

If long = FALSE (default), returns a data frame with columns:

• n: number of sample units.

• s: mean number of species.

• lower: lower confidence interval bound.

• upper: upper confidence interval bound.

If long = TRUE, returns a list with:

• matrix.s: full matrix of species richness per permutation.

• s: the summarized data frame described above.

Note

With long = TRUE, the function provides access to the complete set of resampling results, useful for additional data analyses.

Author(s)

Rodrigo Augusto Santinelo Pereira raspereira@usp.br

Adriano Sanches Melo

References

Magurran, A. E. (1988). Ecological Diversity and Its Measurement. Croom Helm.

Magurran, A. E. (2004). Measuring Biological Diversity. Blackwell Publishing.

Examples

## Using 'formula' (long format)
## Without smoothing confidence intervals
collector.curve(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  times = 1000, long = FALSE, plot = TRUE
)

## Smoothing confidence intervals
collector.curve(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  spar = 0.6, times = 1000, long = FALSE, plot = TRUE
)

## Using different plot themes
collector.curve(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  times = 1000, long = FALSE, plot = TRUE, theme = "theme_light"
)
collector.curve(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  times = 1000, long = FALSE, plot = TRUE, theme = "theme_bw"
)
collector.curve(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  times = 1000, long = FALSE, plot = TRUE, theme = "theme_minimal"
)

## Using a matrix (wide format)
data.matrix <- with(
  quadrat.df,
  table(Plot, Species, exclude = "Morta")
)
collector.curve(x = data.matrix, times = 1000)

## Alternatively...
data.matrix <- as.matrix(
  xtabs(~ Plot + Species, data = quadrat.df, exclude = "Morta")
)
collector.curve(x = data.matrix, times = 1000)

Estimate phytosociological parameters and diversity indices

Description

Estimate the phytosociological parameters and the Shannon–Wiener, Pielou, and Simpson diversity indices, using the quadrat or the point-centered quarter methods.

Usage

phytoparam(
  x,
  measure.label = NULL,
  h = "h",
  taxon = "taxon",
  family = "family",
  dead = "dead",
  circumference = TRUE,
  su = "quadrat",
  height = TRUE,
  quadrat = TRUE,
  su.size,
  d = "distance",
  shape.factor = 1,
  rm.dead = FALSE,
  check.spelling = TRUE
)

Arguments

Details

The function estimates phytosociological parameters for tree communities sampled by quadrat or point-centered quarter methods (quadrat = TRUE or FALSE, respectively).

For the quadrat method, x must contain columns for sample-unit labels, taxon names, and "cbh" or "dbh" measurements for each sampled tree. Additionally, trunk height and family can be included to estimate volume and family-level parameters.

For the point-centered quarter method, x must contain (in addition to the mandatory quadrat columns) a column for the distance from the point to each individual.

The "cbh"/"dbh" column accepts multiple-stem notation, e.g. "17.1+8+5.7+6.8". The plus sign delimits stems. Decimal delimiter may be period or comma; spaces around "+" are ignored. Column names in x are coerced to lowercase at runtime, making matching case-insensitive. If x contains the default column names, the arguments h, taxon, family, dead, su and d can be omitted.

Unbiased absolute density for the point-centered quarter method follows Pollard (1971) and Seber (1982).

Measurement units: individual "cbh"/"dbh" in centimeters; trunk height and point-to-individual distance in meters.

Value

An object of class param with two or four data frames:

• data: A data.frame containing the original community sample data, added with a column of the individual basal area (ABi).

• global: total parameters and diversity indices. Sampled area in hectares (ha), total density in individuals/ha, total dominance in m^2 ha^{-1} (basal area) or m^3 ha^{-1} (volume, when computed), and Shannon–Wiener H' in nats/individual (natural log).

• param: taxon-level table with observed number of individuals (N), absolute/relative density (ADe, RDe), absolute/relative frequency (AFr, RFr), absolute/relative dominance (ADo, RDo), absolute/relative volume (AVol, RVol), Importance Value Index (IV), and Cover Value Index (CV). Absolute parameters per hectare; relative parameters in percentage.

• family: If family != NA, a table listing the number of individuals and the number of species per family is presented.

• vars: A list containing objects used in the functions AGB, stats and stratvol.

References

Pollard, J. H. (1971). On distance estimators of density in randomly distributed forests. Biometrics, 27, 991–1002.

Seber, G. A. F. (1982). The Estimation of Animal Abundance and Related Parameters. New York: Macmillan, pp. 41–45.

See Also

summary.param, plot.param

Examples

## Quadrat method
quadrat.param <- phytoparam(
  x = quadrat.df, measure.label = "CBH", taxon = "Species",
  dead = "Morta", family = "Family", circumference = TRUE, su = "Plot",
  height = TRUE, su.size = 25, rm.dead = FALSE
)
summary(quadrat.param)
head(quadrat.param$data)
quadrat.param$global
quadrat.param$family
quadrat.param$param

## Point-centered quarter method
point.param <- phytoparam(
  x = point.df, measure.label = "CBH", taxon = "Species",
  dead = "Morta", family = "Family", circumference = TRUE, su = "Point",
  height = TRUE, quadrat = FALSE, d = "Distance", rm.dead = FALSE
)
summary(point.param)
head(point.param$data)
point.param$global
point.param$family
point.param$param

## Using default column names
point.default <- point.df
colnames(point.default) <- c("point", "family", "taxon", "distance", "cbh", "h")
point.param.default <- phytoparam(
  x = point.default, dead = "morta",
  circumference = TRUE, height = TRUE, quadrat = FALSE
)
summary(point.param.default)
point.param.default$global

## Plotting
plot(quadrat.param)
plot(point.param)
plot(point.param, theme = "theme_light")
plot(point.param, theme = "theme_bw")
plot(point.param, theme = "theme_minimal")

Plot relative phytosociological parameters by taxon

Description

Produce a stacked bar chart of relative dominance (RDo), relative frequency (RFr), and relative density (RDe) for each taxon contained in a param object returned by phytoparam. Taxa are ordered by the Importance Value (IV).

Usage

## S3 method for class 'param'
plot(x, theme = "theme_classic", ...)

Arguments

Details

The function reshapes the taxon-level table to long format and draws a horizontal stacked bar chart (RDo, RFr, RDe) with taxa ordered by increasing IV.

Value

A ggplot object.

See Also

phytoparam, summary.param, and ggplot2.

Examples

res <- phytoparam(x = quadrat.df, measure.label = "CBH", taxon = "Species",
                  dead = "Morta", family = "Family", circumference = TRUE,
                  su = "Plot", height = TRUE, su.size = 25)
plot(res)                        # default theme (theme_classic)
plot(res, theme = "theme_light") # theme by name
plot(res, theme = ggplot2::theme_minimal()) # theme object

Point-centered quarter dataset

Description

A dataset containing tree measurements collected using the point-centered quarter method at 25 sampling points in a forest fragment located at the campus of the University of Sao Paulo, Ribeirao Preto, Brazil. At each point, the nearest tree in each of the four quadrants was sampled, totaling 100 individuals.

Usage

data(point.df)

Format

A data frame with 100 rows and 6 variables:

Point

Sampling point identifier (1–25).

Family

Botanical family of the tree.

Species

Scientific name of the species.

Distance

Distance from the point to the tree (m).

CBH

Circumference at breast height (cm).

h

Total height of the tree (m).

Forest inventory dataset from 25 quadrats

Description

A dataset containing tree measurements from 25 quadrats (5 × 5 m each) sampled in a forest fragment located at the campus of the University of Sao Paulo, Ribeirao Preto, Brazil.

Usage

data(quadrat.df)

Format

A data frame with 171 rows and 5 variables:

Plot

Quadrat identifier (e.g., X2Y3).

Family

Botanical family of the tree.

Species

Scientific name of the species.

CBH

Circumference at breast height (cm).

h

Total height of the tree (m).

Quadrat dataset with coordinates for basal area plotting

Description

A dataset containing tree measurements from 25 quadrats (5 × 5 m each) sampled in a forest fragment located at the campus of the University of Sao Paulo, Ribeirao Preto, Brazil. This dataset is organized to demonstrate the use of the function BAplot() for visualizing basal areas. In addition to tree measurements, it provides the spatial coordinates of the lower-left corner of each quadrat.

Usage

data(quadrat2_plot.df)

Format

A data frame with 219 rows and 6 variables:

Plot

Quadrat identifier (e.g., X1Y1).

CBH

Circumference at breast height (cm).

h

Total height of the tree (m).

Species

Scientific name of the species.

x

X coordinate (m) of the lower-left corner of the quadrat.

y

Y coordinate (m) of the lower-left corner of the quadrat.

Quadrat dataset with simulated individual tree coordinates

Description

A dataset containing tree measurements from 25 quadrats (5 × 5 m each) sampled in a forest fragment located at the campus of the University of Sao Paulo, Ribeirao Preto, Brazil. This dataset corresponds to the same trees as in quadrat2_plot.df, but includes simulated coordinates (in meters) for each individual tree inside the quadrats. It is organized to demonstrate the use of the function BAplot() with the argument ind.coord = TRUE.

Usage

data(quadrat2_tree.df)

Format

A data frame with 219 rows and 7 variables:

Plot

Quadrat identifier (e.g., X1Y1).

CBH

Circumference at breast height (cm).

h

Total height of the tree (m).

Species

Scientific name of the species.

x

Simulated X coordinate (m) of the tree inside the quadrat.

y

Simulated Y coordinate (m) of the tree inside the quadrat.

Rectangular quadrat dataset

Description

A dataset containing tree measurements from 25 rectangular quadrats (20 × 10 m each) sampled in a seasonal semideciduous forest fragment in the State of Sao Paulo, Brazil. The dataset includes total height and commercial bole height of trees, along with the spatial coordinates of the lower-left corner of each quadrat. It is organized to demonstrate the use of the function BAplot() with rectangular quadrats.

Usage

data(quadrat3_rect.df)

Format

A data frame with 497 rows and 8 variables:

Plot

Quadrat identifier.

x

X coordinate (m) of the lower-left corner of the quadrat.

y

Y coordinate (m) of the lower-left corner of the quadrat.

Species

Scientific name of the species.

Family

Botanical family of the tree.

DBH

Diameter at breast height (cm).

h

Total height of the tree (m).

hcom

Commercial bole height of the tree (m).

Rarefaction Analysis

Description

Performs a rarefaction analysis, a method widely used in ecology to estimate species richness based on sample size. The function computes the expected number of species for increasing numbers of individuals, along with confidence intervals, following classical approaches by Hurlbert (1971), Heck et al. (1975), and related developments.

Usage

rarefaction(
  formula,
  data,
  x,
  step = 1,
  points = NULL,
  prob = 0.95,
  xlab,
  ylab,
  plot = TRUE,
  theme = "theme_classic"
)

Arguments

Details

Rarefaction analysis provides a standardized way to compare species richness among samples of different sizes. It is based on probabilistic resampling without replacement and produces an expected species accumulation curve. Confidence intervals are calculated following variance estimators proposed by Heck et al. (1975) and Tipper (1979).

The function accepts data in three formats:

• long format (formula + data arguments),

• contingency matrix,

• vector of individuals per species.

Dead individuals can be excluded by specifying an additional term in the formula.

Value

A data frame with the following components:

• n: number of sample units.

• s: mean number of species.

• lower: lower confidence interval bound.

• upper: upper confidence interval bound.

If plot = TRUE, a rarefaction curve with confidence ribbons is produced using ggplot2.

Author(s)

Rodrigo Augusto Santinelo Pereira raspereira@usp.br

References

Colwell, R. K., Mao, C. X., & Chang, J. (2004). Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology, 85(10), 2717–2727. doi:10.1890/03-0557

Heck, K. L., Van Belle, G., & Simberloff, D. (1975). Explicit calculation of the rarefaction diversity measurement and the determination of sufficient sample size. Ecology, 56(6), 1459–1461. doi:10.2307/1934716

Hurlbert, S. H. (1971). The nonconcept of species diversity: A critique and alternative parameters. Ecology, 52(4), 577–586. doi:10.2307/1934145

Tipper, J. C. (1979). Rarefaction and rarefiction—The use and abuse of a method in paleoecology. Paleobiology, 5(4), 423–434. doi:10.1017/S0094837300016924

Examples

## Using 'formula' (long format)
rarefaction(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  plot = TRUE
)


## Using different plot themes
rarefaction(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  plot = TRUE,
  theme = "theme_light"
)
rarefaction(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  plot = TRUE,
  theme = "theme_bw"
)
rarefaction(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  plot = TRUE,
  theme = "theme_minimal"
)

## Using a matrix (wide format)
data.matrix <- with(
  quadrat.df,
  table(Plot, Species, exclude = "Morta")
)
rarefaction(x = data.matrix, plot = TRUE)

data.matrix <- as.matrix(
  xtabs(~ Plot + Species, data = quadrat.df, exclude = "Morta")
)
rarefaction(x = data.matrix, plot = TRUE)

## Using a vector
data.vector <- sort(
  as.vector(apply(data.matrix, 2, sum)),
  decreasing = TRUE
)
rarefaction(x = data.vector, plot = TRUE)

## Using breakpoints
pts <- c(1, 10, 30, 50, 80)
rarefaction(
  formula = Species ~ Plot - Morta,
  data = quadrat.df,
  points = pts,
  plot = TRUE
)
rarefaction(x = data.matrix, points = pts, plot = TRUE)
rarefaction(
  x = data.vector,
  points = pts,
  plot = TRUE,
  theme = "theme_light"
)
rarefaction(x = data.vector, points = 50, plot = FALSE)

Representativeness and confidence statistics for a forest inventory

Description

Computes representativeness and confidence statistics for a forest inventory. Summarizes inventory coverage, estimates absolute stand parameters per hectare with uncertainty (density, volume, basal area), and extrapolates results to the total habitat area, including sample-size requirements for target permissible errors.

Usage

stats(obj, area.tot, prob = 0.95, shape.factor = 1, rm.dead = FALSE)

Arguments

Details

Extracts the sample-unit (SU) identifier, taxon label, height/length variable, and “dead” code from obj$vars, and the surveyed (inventoried) area from obj$global. If rm.dead = TRUE, individuals flagged as dead are removed before analysis.

Individual volume is computed as V_i = abi \times h \times shape.factor, where abi and h are columns present in obj$data. Per-SU totals of volume and basal area are converted to per-hectare values using the SU area. The function then derives, for density (ADe), volume (AVol), and basal area (ABA):

• mean, variance, standard deviation, standard error;

• coefficient of variation (CV);

• absolute and relative sampling error (based on the t quantile with |SU| - 1 df);

• lower and upper confidence limits per hectare.

Inventory representativeness (total area, number/area of SUs, and percentage inventoried) is reported, and population totals (for area.tot) are produced with corresponding confidence limits. Required numbers of SUs to attain 10% and 20% permissible relative errors are computed from standard finite-population sampling formulae.

Value

A list with three components:

• inventory: data frame summarizing inventory coverage (fields Stat and Values).

• ha: data frame of per-hectare statistics for stand parameters (fields Stat, ADe, AVol, ABA).

• population: data frame of population-level statistics for the total area (area.tot), including required numbers of SUs for 10% and 20% permissible errors.

Note

• Representativeness is reported both as inventoried area (ha) and as percentage of the total area.

• The number of SUs required for a target permissible error depends on the observed variance, the SU area, and the chosen confidence level (prob).

Author(s)

Rodrigo Augusto Santinelo Pereira raspereira@usp.br

References

FAO (1981). Manual of Forest Inventory—With Special Reference to Mixed Tropical Forests. Food and Agriculture Organization of the United Nations, Rome.

Examples

## Creating the param object containing the phytosociological parameters
quadrat.param <- phytoparam(x = quadrat.df, measure.label = "CBH",
                            taxon = "Species", dead = "Morta", family = "Family",
                            circumference = TRUE, su = "Plot", height = TRUE,
                            su.size = 25, rm.dead = FALSE)

## Calculating the statistics
stats(obj = quadrat.param, area.tot = 4)

Stratified wood volume by DBH classes

Description

stratvol computes wood volume (m^3 ha^{-1}) stratified by diameter at breast height (DBH, in centimeters) classes for each taxon in a forest inventory. Individual tree volume is calculated as V_i = ABi \times h \times shape.factor, where ABi is the individual basal area at breast height and h is tree height. Volumes are then summed within DBH classes and standardized per hectare using the inventoried area stored in obj.

Usage

stratvol(obj, classes = 20, shape.factor = 1, rm.dead = FALSE)

Arguments

Details

- DBH classes are defined from the numeric breakpoints provided in classes, using closed–open intervals internally and labeled for readability as: <=a, ]a-b], …, >z (all in centimeters). - Individual volume is computed as V_i = ABi \times h \times shape.factor. Summed volumes per class are divided by the inventoried area (in hectares) retrieved from obj$global, yielding m^3 ha^{-1}.

Value

A data.frame with one row per taxon and the following columns:

• Taxon: Taxon label.

• One column per DBH class (labeled in centimeters), containing the summed wood volume per hectare (m^3 ha^{-1}) for that taxon within the class. Missing combinations are returned as 0.

Note

• Units: Supply size classes in centimeters. The function assumes ABi and h yield volume in cubic meters before per-hectare standardization.

• Class limits: Class breakpoints should not exceed the maximum observed DBH; otherwise the function stops with an error.

Author(s)

Rodrigo Augusto Santinelo Pereira (raspereira@usp.br)

References

FAO (1981). Manual of forest inventory—With special reference to mixed tropical forests. Food and Agriculture Organization of the United Nations.

See Also

phytoparam

Examples

# Creating the 'param' object with phytosociological parameters
point.param <- phytoparam(x = point.df, measure.label = "CBH",
                          taxon = "Species", dead = "Morta", family = "Family",
                          circumference = TRUE, su = "Point", height = TRUE,
                          quadrat = FALSE, d = "Distance", rm.dead = FALSE)

# Stratified volumes with a single breakpoint (<= 20 cm; > 20 cm)
stratvol(point.param, classes = 20)

# Stratified volumes with multiple classes (<= 5], ]5–10], > 10 cm)
stratvol(point.param, classes = c(5, 10))

# Using a taper/form correction factor and excluding dead trees
stratvol(point.param, classes = c(10, 20, 30), shape.factor = 0.7, rm.dead = TRUE)

Summarize global phytosociological parameters

Description

Display a concise summary of the global parameters computed by phytoparam. If family-level outputs are present (i.e., the string "N. of families" occurs in the first column of object$global), the first seven rows are shown; otherwise, the first six rows are shown.

Usage

## S3 method for class 'param'
summary(object, ...)

Arguments

Details

Row names of object$global are removed before printing. The function is intended for quick inspection of the main global metrics.

Value

Used mainly for its side effect of printing to the console. Invisibly returns the displayed data.frame.

See Also

PhytoIn (phytoparam, plot.param.

Examples

res <- phytoparam(x = quadrat.df, measure.label = "CBH",
                  taxon = "Species", family = "Family",
                  su = "Plot", su.size = 25)
summary(res)  # calls summary.param (S3)