rFIA (version 0.1.1)

diversity: Estimate species diversity from FIADB

Description

Produces estimates of species diversity from FIA data. Returns shannon's index, shannon's equitability, and species richness for alpha (mean/SE of stands), beta, and gamma diversity. Estimates can be produced for regions defined within the FIA Database (e.g. counties), at the plot level, or within user-defined areal units. Options to group estimates by size class and other variables defined in the FIADB. If multiple reporting years (EVALIDs) are included in the data, estimates will be output as a time series. If multiple states are represented by the data, estimates will be output for the full region (all area combined), unless specified otherwise (e.g. grpBy = STATECD). Easy options to implement parallel processing.

Usage

diversity(db, grpBy = NULL, polys = NULL, returnSpatial = FALSE, bySizeClass = FALSE,
          landType = 'forest', treeType = 'live', treeDomain = NULL,
          areaDomain = NULL, byPlot = FALSE, SE = TRUE, nCores = 1)

Arguments

db

FIA.Database object produced from readFIA. Function requires that PLOT, TREE, COND, POP_PLOT_STRATUM_ASSGN, POP_ESTN_UNIT, POP_EVAL, POP_STRATUM, POP_EVAL_TYP, POP_EVAL_GRP tables exist in FIA.Database object.

grpBy

variables from PLOT, COND, or TREE tables to group estimates by (NOT quoted). Multiple grouping variables should be combined with c(), and grouping will occur heirarchically. For example, to produce seperate estimates for each ownership group within ecoregion subsections, specify c(ECOSUBCD, OWNGRPCD).

polys

sp or sf Polygon/MultiPolgyon object; Areal units to bin data for estimation. Seperate estimates will be produces for region encompassed by each areal unit.

returnSpatial

logical; if TRUE, return sf spatial object (polys must also be specified).

bySizeClass

logical; if TRUE, returns estimates grouped by size class (default 2-inch intervals, see makeClasses to compute other size class intervals).

landType

character ('forest' or 'timber'); Type of land which estimates will be produced for. Timberland is a subset of forestland (default) which has high site potential and non-reserve status (see details).

treeType

character ('all', 'live', 'dead', or 'gs'); Type of tree which estimates will be produced for. All (default) includes all stems, live and dead, greater than 1 in. DBH. Live/Dead includes all stems greater than 1 in. DBH which are live or dead (leaning less than 45 degrees), respectively. GS (growing-stock) includes live stems greater than 5 in. DBH which contain at least one 8 ft merchantable log.

treeDomain

logical predicates defined in terms of the variables in PLOT, TREE, and/or COND tables. Used to define the type of trees for which estimates will be produced (e.g. DBH greater than 20 inches: DIA > 20, Dominant/Co-dominant crowns only: CCLCD %in% c(2,3)). Multiple conditions are combined with & (and) or | (or). Only trees where the condition evaluates to TRUE are used in producing estimates. Should NOT be quoted.

areaDomain

logical predicates defined in terms of the variables in PLOT and/or COND tables. Used to define the area for which estimates will be produced (e.g. within 1 mile of improved road: RDDISTCD %in% c(1:6), Hard maple/basswood forest type: FORTYPCD == 805). Multiple conditions are combined with & (and) or | (or). Only plots within areas where the condition evaluates to TRUE are used in producing estimates. Should NOT be quoted.

byPlot

logical; if TRUE, returns estimates for individual plot locations (population totals not returned).

SE

logical; if TRUE, returns estimates with samping error (approx. 5x faster without returning samping errors)

nCores

numeric; number of cores to use for parallel implementation. Check available cores using detectCores. Default = 1, serial processing.

Value

Dataframe or SF object (if returnSpatial = TRUE). If byPlot = TRUE, indices are returned for each plot. All variables with names ending in SE, represent the estimate of sampling error (%) of the variable.

• H_a: mean Shannon's Diversity Index, alpha (stand) level

• H_b: Shannon's Diversity Index, beta (landscape) level

• H_g: Shannon's Diversity Index, gamma (regional) level

• Eh_a: mean Shannon's Equitability Index, alpha (stand) level

• Eh_b: Shannon's Equitability Index, beta (landscape) level

• Eh_g: Shannon's Equitability Index, alpha (stand) level

• S_a: mean Species Richness, alpha (stand) level

• S_b: Species Richness, beta (landscape) level

• S_g: Species Richness, gamma (regional) level

• nStands: number of stands with non-zero plots used to compute alpha diversity estimates

Details

Procedures for computing diversity indices are outlined in Hill (1973) and Shannon (1948), and estimation of mean/ sampling error follows the procedures documented in Bechtold and Patterson (2005).

Alpha-level indices are computed as the mean (SE) species diversity of a stand or condition as defined by FIA. Specifically, alpha diversity is estimated using a sample-based ratio-of-means estimator of stand diversity (e.g. Richness) * land area of stand / total land area within the domain of interest. Thus estimates of alpha diversity within a stand are weighted by the area which that stand represents. Gamma-level diversity is computed as regional indicies, pooling all plot data. Beta diversity is computed as gamma diversity - alpha diversity, and thus represents the excess of regional diversity with respect to local diversity. As stems of various size classes do not have equal probability of detection under the National FIA plot design, we compute relative proportions of species abundances based on the trees per acre they represent.

Stratified random sampling techniques are most often employed to compute estimates in recent inventories, although double sampling and simple random sampling may be employed for early inventories. Estimates are adjusted for non-response bias by assuming attributes of non-response plot locations to be equal to the mean of other plots included within thier respective stratum or population.

Forest land must be at least 10-percent stocked by trees of any size, including land that formerly had such tree cover and that will be naturally or artificially regenerated. Forest land includes transition zones, such as areas between heavily forested and nonforested lands that are at least 10-percent stocked with trees and forest areas adjacent to urban and builtup lands. The minimum area for classification of forest land is 1 acre and 120 feet wide measured stem-to-stem from the outer-most edge. Unimproved roads and trails, streams, and clearings in forest areas are classified as forest if less than 120 feet wide. Timber land is a subset of forest land that is producing or is capable of producing crops of industrial wood and not withdrawn from timber utilization by statute or administrative regulation. (Note: Areas qualifying as timberland are capable of producing at least 20 cubic feet per acre per year of industrial wood in natural stands. Currently inaccessible and inoperable areas are NOT included).

Easy, efficient parallelization is implemented with the parallel package. Users must only specify the nCores argument with a value greater than 1 in order to implement parallel processing on their machines. Parallel implementation is achieved using a snow type cluster on any Windows OS, and with multicore forking on any Unix OS (Linux, Mac). Implementing parallel processing may substantially decrease free memory during processing, particularly on Windows OS. Thus, users should be cautious when running in parallel, and consider implementing serial processing for this task if computational resources are limited (nCores = 1).

References

FIA Database User Guide: https://www.fia.fs.fed.us/library/database-documentation/

Bechtold, W.A.; Patterson, P.L., eds. 2005. The Enhanced Forest Inventory and Analysis Program - National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS - 80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 85 p. https://www.srs.fs.usda.gov/pubs/gtr/gtr_srs080/gtr_srs080.pdf

Analysis of ecological communities. (2002). United States: M G M SOFTWARE DESIGN (OR).

Hill, M. O. (1973). Diversity and Evenness: A Unifying Notation and Its Consequences. Ecology, 54(2), 427-432. doi:10.2307/1934352.

Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x.

See Also

tpa, standStruct, invasive

Examples

# NOT RUN {
## Load data from rFIA package
data(fiaRI)
data(countiesRI)

## Make a most recent subset
fiaRI_mr <- clipFIA(fiaRI)

## Most recent estimates for live stems on forest land
diversity(db = fiaRI_mr,
          landType = 'forest',
          treeType = 'live')

## Most recent estimates grouped by stand age on forest land
# Make a categorical variable which represents stand age (grouped by 10 yr intervals)
fiaRI_mr$COND$STAND_AGE <- makeClasses(fiaRI_mr$COND$STDAGE, interval = 10)
diversity(db = fiaRI_mr,
          grpBy = STAND_AGE)

# }
# NOT RUN {
## Estimates for live white pine ( > 12" DBH) on forested mesic sites (all available inventories)
diversity(fiaRI,
          treeType = 'live',
          treeDomain = DIA > 12,
          areaDomain = PHYSCLCD %in% 21:29) # Mesic Physiographic classes

## Most recent estimates for growing-stock on timber land by species
diversity(db = fiaRI_mr,
          landType = 'timber',
          treeType = 'gs',
          bySizeClass = TRUE)

## Same as above, implemented in parallel
parallel::detectCores(logical = FALSE) # 4 cores available, we will take 2
diversity(db = fiaRI_mr,
          landType = 'timber',
          treeType = 'gs',
          bySizeClass = TRUE,
          nCores = 2)

## Most recent estimates for all stems on forest land grouped by user-defined areal units
ctSF <- diversity(clipFIA(fiaRI, mostRecent = TRUE),
                  polys = countiesRI,
                  returnSpatial = TRUE)
plot(ctSF) # Plot multiple variables simultaneously
plotFIA(ctSF, H_a) # Plot of mean Shannons Index of stands
# }