Estimate invasive species coverage from FIADB

Produces estimates of areal coverage of invasive species from the Forest Inventory and Analysis Database. Estimates can be produced for regions defined within the FIA Database (e.g. counties), at the plot level, or within user-defined areal units. All estimates are returned by species although can be grouped by 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).

Usage

invasive(db, grpBy = NULL, polys = NULL, returnSpatial = FALSE, landType = "forest",
         method = 'TI', lambda = 0.5, areaDomain = NULL, totals = FALSE,
         variance = FALSE, byPlot = FALSE, nCores = 1)

Arguments

db: FIA.Database or Remote.FIA.Database object produced from readFIA or getFIA. If a Remote.FIA.Database, data will be read in and processed state-by-state to conserve RAM (see details for an example).
grpBy: variables from PLOT or COND 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 methods of stand regeneration, specify c(STDORGCD, OWNGRPCD).
polys: sp or sf Polygon/MultiPolgyon object; Areal units to bin data for estimation. Separate estimates will be produced for region encompassed by each areal unit. FIA plot locations will be reprojected to match projection of polys object.
returnSpatial: logical; if TRUE, merge population estimates with polys and return as sf multipolygon object. When byPlot = TRUE, return plot-level estimates as sf spatial points.
landType: character ("forest" or "timber"); Type of land that estimates will be produced for. Timberland is a subset of forestland (default) which has high site potential and non-reserve status (see details).
method: character; design-based estimator to use. One of: "TI" (temporally indifferent, default), "annual" (annual), "SMA" (simple moving average), "LMA" (linear moving average), or "EMA" (exponential moving average). See Stanke et al 2020 for a complete description of these estimators.
lambda: numeric (0,1); if method = 'EMA', the decay parameter used to define weighting scheme for annual panels. Low values place higher weight on more recent panels, and vice versa. Specify a vector of values to compute estimates using mulitple wieghting schemes, and use plotFIA with grp set to lambda to produce moving average ribbon plots. See Stanke et al 2020 for examples.
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.
totals: logical; if TRUE, return total population estimates (e.g. total area) along with ratio estimates (e.g. mean trees per acre).
variance: logical; if TRUE, return estimated variance (VAR) and sample size (N). If FALSE, return 'sampling error' (SE) as returned by EVALIDator. Note: sampling error cannot be used to construct confidence intervals.
byPlot: logical; if TRUE, returns estimates for individual plot locations instead of population estimates.
nCores: numeric; number of cores to use for parallel implementation. Check available cores using detectCores. Default = 1, serial processing.

Details

Estimation Details

Estimation of forest variables follows the procedures documented in Bechtold and Patterson (2005) and Stanke et al 2020.

Specifically, percent areal coverage is computed using a sample-based ratio-of-means estimator of total invasive coverage area / total land area within the domain of interest. Estimates of areal coverage of individual invasive species should NOT be summed to produce estimates of areal coverage by ALL invasive species, as areal coverage by species is not mutually exclusive (multiple species may occur in the same area). Current FIA data collection protocols do not allow for the unbiased estimation of areal coverage by all invasive species.

Users may specify alternatives to the 'Temporally Indifferent' estimator using the method argument. Alternative design-based estimators include the annual estimator ("ANNUAL"; annual panels, or estimates from plots measured in the same year), simple moving average ("SMA"; combines annual panels with equal weight), linear moving average ("LMA"; combine annual panels with weights that decay linearly with time since measurement), and exponential moving average ("EMA"; combine annual panels with weights that decay exponentially with time since measurement). The "best" estimator depends entirely on user-objectives, see Stanke et al 2020 for a complete description of these estimators and tradeoffs between precision and temporal specificity.

When byPlot = FALSE (i.e., population estimates are returned), the "YEAR" column in the resulting dataframe indicates the final year of the inventory cycle that estimates are produced for. For example, an estimate of current forest area (e.g., 2018) may draw on data collected from 2008-2018, and "YEAR" will be listed as 2018 (consistent with EVALIDator). However, when byPlot = TRUE (i.e., plot-level estimates returned), the "YEAR" column denotes the year that each plot was measured (MEASYEAR), which may differ slightly from its associated inventory year (INVYR).

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.

Working with "Big Data"

If FIA data are too large to hold in memory (e.g., R throws the "cannot allocate vector of size ..." errors), use larger-than-RAM options. See documentation of link{readFIA} for examples of how to set up a Remote.FIA.Database. As a reference, we have used rFIA's larger-than-RAM methods to estimate forest variables using the entire FIA Database (~50GB) on a standard desktop computer with 16GB of RAM. Check out our website for more details and examples.

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).

Definition of forestland

Forest land must have at least 10-percent canopy cover by live tally 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 forest and non-forested lands that meet the mimium tree canopy cover and forest areas adjacent to urban and built-up lands. The minimum area for classification of forest land is 1 acre in size and 120 feet wide measured stem-to-stem from the outer-most edge. Roadside, streamside, and shelterbelt strips of trees must have a width of at least 120 feet and continuous length of at least 363 feet to qualify as forest land. Tree-covered areas in agricultural production settings, such as fruit orchards, or tree-covered areas in urban settings, such as city parks, are not considered forest land.

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).

Value

Dataframe or sf object (if returnSpatial = TRUE). If byPlot = TRUE, values are returned for each plot (proportion of plot in domain of interest; PLOT_STATUS_CD = 1 when forest exists at the plot location). All variables with names ending in SE, represent the estimate of sampling error (%) of the variable. When variance = TRUE, variables ending in VAR denote the variance of the variable and N is the total sample size (i.e., including non-zero plots).

YEAR: reporting year associated with estimates
SYMBOL: unique species ID from NRCS Plant Reference Guide
SCIENTIFIC_NAME: scientific name of the species
COMMON_NAME: common name of the species
COVER_PCT: estimate of percent areal coverage of the species
COVER_AREA: estimate of areal coverage of the species (acres)
AREA: estimate of total land area (acres)
nPlots_INV: number of non-zero plots used to compute invasive coverage estimates
nPlots_AREA: number of non-zero plots used to compute land area estimates

References

rFIA website: https://rfia.netlify.app/

FIA Database User Guide: https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi

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

Stanke, H., Finley, A. O., Weed, A. S., Walters, B. F., & Domke, G. M. (2020). rFIA: An R package for estimation of forest attributes with the US Forest Inventory and Analysis database. Environmental Modelling & Software, 127, 104664.

Author

Hunter Stanke and Andrew Finley

Note

All sampling error estimates (SE) are returned as the "percent coefficient of variation" (standard deviation / mean * 100) for consistency with EVALIDator. IMPORTANT: sampling error cannot be used to construct confidence intervals. Please use variance = TRUE for that (i.e., return variance and sample size instead of sampling error).

Examples


## Load data from the rFIA package
data(fiaRI)
data(countiesRI)

## Most recents subset
fiaRI_mr <- clipFIA(fiaRI)

# \donttest{
## Most recent estimates on forest land
invasive(db = fiaRI_mr,
         landType = 'forest')
#> # A tibble: 6 × 9
#>    YEAR SYMBOL SCIENTIFIC_NAME     COMMON_NAME COVER_PCT COVER_PCT_SE nPlots_INV
#>   <dbl> <chr>  <chr>               <chr>           <dbl>        <dbl>      <int>
#> 1  2018 BETH   Berberis thunbergii Japanese b…    0.0561         73.5          2
#> 2  2018 CEOR7  Celastrus orbicula… Oriental b…    0.767          85.7          3
#> 3  2018 FRAL4  Frangula alnus      glossy buc…    0.135          91.1          1
#> 4  2018 POCU6  Polygonum cuspidat… Japanese k…    0.103         105.           1
#> 5  2018 RHCA3  Rhamnus cathartica  common buc…    0.0248        105.           1
#> 6  2018 ROMU   Rosa multiflora     multiflora…    0.341          66.5          3
#> # ℹ 2 more variables: nPlots_AREA <int>, N <int>

## Most recent estimates on forest land
invasive(db = fiaRI_mr,
         landType = 'forest',
         byPlot = TRUE)
#> # A tibble: 11 × 8
#>     PLT_CN  YEAR pltID      SYMBOL SCIENTIFIC_NAME    COMMON_NAME PROP_INV_COVER
#>      <dbl> <int> <chr>      <chr>  <chr>              <chr>                <dbl>
#>  1 1.45e13  2013 1_44_7_169 CEOR7  Celastrus orbicul… Oriental b…        0.01   
#>  2 1.45e13  2013 1_44_7_169 FRAL4  Frangula alnus     glossy buc…        0.01   
#>  3 1.45e13  2013 1_44_7_169 ROMU   Rosa multiflora    multiflora…        0.01   
#>  4 1.68e14  2014 1_44_7_254 CEOR7  Celastrus orbicul… Oriental b…        0.0615 
#>  5 1.68e14  2014 1_44_7_254 POCU6  Polygonum cuspida… Japanese k…        0.03   
#>  6 1.68e14  2014 1_44_7_254 RHCA3  Rhamnus cathartica common buc…        0.00723
#>  7 1.68e14  2015 1_44_7_113 BETH   Berberis thunberg… Japanese b…        0.01   
#>  8 1.68e14  2015 1_44_7_113 ROMU   Rosa multiflora    multiflora…        0.01   
#>  9 3.74e14  2017 1_44_5_102 BETH   Berberis thunberg… Japanese b…        0.00423
#> 10 3.74e14  2017 1_44_5_102 CEOR7  Celastrus orbicul… Oriental b…        0.0338 
#> 11 3.74e14  2017 1_44_5_102 ROMU   Rosa multiflora    multiflora…        0.0480 
#> # ℹ 1 more variable: PROP_FOREST <dbl>

## Same as above, but implemented in parallel (much quicker)
parallel::detectCores(logical = FALSE) # 4 cores available, we will take 2
#> [1] 16
invasive(db = fiaRI_mr,
         landType = 'forest',
         nCores = 2)
#> # A tibble: 6 × 9
#>    YEAR SYMBOL SCIENTIFIC_NAME     COMMON_NAME COVER_PCT COVER_PCT_SE nPlots_INV
#>   <dbl> <chr>  <chr>               <chr>           <dbl>        <dbl>      <int>
#> 1  2018 BETH   Berberis thunbergii Japanese b…    0.0561         73.5          2
#> 2  2018 CEOR7  Celastrus orbicula… Oriental b…    0.767          85.7          3
#> 3  2018 FRAL4  Frangula alnus      glossy buc…    0.135          91.1          1
#> 4  2018 POCU6  Polygonum cuspidat… Japanese k…    0.103         105.           1
#> 5  2018 RHCA3  Rhamnus cathartica  common buc…    0.0248        105.           1
#> 6  2018 ROMU   Rosa multiflora     multiflora…    0.341          66.5          3
#> # ℹ 2 more variables: nPlots_AREA <int>, N <int>

## 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)
invasive(db = fiaRI_mr,
         grpBy = STAND_AGE)
#> # A tibble: 10 × 10
#>     YEAR STAND_AGE SYMBOL SCIENTIFIC_NAME     COMMON_NAME COVER_PCT COVER_PCT_SE
#>    <dbl> <chr>     <chr>  <chr>               <chr>           <dbl>        <dbl>
#>  1  2018 [70,80)   CEOR7  Celastrus orbicula… Oriental b…    1.27           90.5
#>  2  2018 [70,80)   FRAL4  Frangula alnus      glossy buc…    0.253          82.1
#>  3  2018 [70,80)   POCU6  Polygonum cuspidat… Japanese k…    0.192          99.1
#>  4  2018 [70,80)   RHCA3  Rhamnus cathartica  common buc…    0.0464         99.1
#>  5  2018 [70,80)   ROMU   Rosa multiflora     multiflora…    0.0843         82.1
#>  6  2018 [80,90)   BETH   Berberis thunbergii Japanese b…    0.106           0  
#>  7  2018 [80,90)   CEOR7  Celastrus orbicula… Oriental b…    0.845           0  
#>  8  2018 [80,90)   ROMU   Rosa multiflora     multiflora…    2.40            0  
#>  9  2018 [90,100)  BETH   Berberis thunbergii Japanese b…    0.125          71.8
#> 10  2018 [90,100)  ROMU   Rosa multiflora     multiflora…    0.125          71.8
#> # ℹ 3 more variables: nPlots_INV <int>, nPlots_AREA <int>, N <int>

## Estimates on forested mesic sites (all available inventories)
invasive(fiaRI,
         areaDomain = PHYSCLCD %in% 21:29) # Mesic Physiographic classes
#> # A tibble: 37 × 9
#>     YEAR SYMBOL SCIENTIFIC_NAME    COMMON_NAME COVER_PCT COVER_PCT_SE nPlots_INV
#>    <dbl> <chr>  <chr>              <chr>           <dbl>        <dbl>      <int>
#>  1  2013 BETH   Berberis thunberg… Japanese b…    0.0117         89.6          1
#>  2  2013 CEOR7  Celastrus orbicul… Oriental b…    0.156          67.7          5
#>  3  2013 ELUM   Elaeagnus umbella… autumn oli…    0.0247        114.           1
#>  4  2013 FRAL4  Frangula alnus     glossy buc…    2.23           96.6          4
#>  5  2013 RHCA3  Rhamnus cathartica common buc…    0.0117         89.6          1
#>  6  2013 ROMU   Rosa multiflora    multiflora…    0.166          60.8          4
#>  7  2013 ROPS   Robinia pseudoaca… black locu…    0.173         114.           1
#>  8  2014 BETH   Berberis thunberg… Japanese b…    0.0116         90.6          1
#>  9  2014 CEOR7  Celastrus orbicul… Oriental b…    0.393          73.7          4
#> 10  2014 FRAL4  Frangula alnus     glossy buc…    2.46           93.5          3
#> # ℹ 27 more rows
#> # ℹ 2 more variables: nPlots_AREA <int>, N <int>
# }