Estimating individual-tree and population growth rates
Hunter Stanke, Jeffrey W. Doser
2021 (last updated February 7, 2025)
Source:vignettes/growth.Rmd
growth.RmdWe can use FIA data to estimate how forest structure has changed over
time (i.e., has density increased or declined) or to estimate the
average rate at which individual trees grow. Here we show how we can use
the vitalRates() function in rFIA to do just
that.
For this specific example, we’ll focus on estimating average annual change in individual tree basal area and cumulative tree basal area per acre, by site productivity class (SITECLCD) and species across the states of Washington and Oregon.
So first, we’ll need to download some data (you can skip this step if
you already have FIA data for WA and OR). Here we use
getFIA() to download and save the state subsets to our
computer:
library(rFIA)
library(dplyr)
# Download some FIA data from the DataMart ------------------------------------
getFIA(states = c('OR', 'WA'),
dir = 'path/to/save/FIA/data',
load = FALSE) # Download, but don't load yetNext we’ll read our data into R with readFIA() and take
a most recent subset across the two states with clipFIA().
Note that we are setting inMemory = FALSE to set up the
database in a remote fashion as opposed to reading the entire data set
into RAM. You would need to set inMemory = TRUE if you
desire to modify columns in the database or do any other sort of
manipualation to the underlying data.
db <- readFIA(dir = fiaPath,
states = c('OR', 'WA'), # If you keep all your data together
nCores = 1, # Can set to >1 if you have multiple cores.
inMemory = FALSE) # Set to TRUE if your computer has enough RAM
# Take the most recent subset -------------------------------------------------
db <- clipFIA(db, mostRecent = TRUE)Now that we have our data loaded, we can start estimating tree growth
rates! First, we’ll estimate net growth rates, i.e., we will
include trees that have recruited or died in our estimates of tree
growth. Note that net growth can be negative if mortality exceeds
recruitment and growth on live trees. This type of growth estimate is
most useful if we’re interested in characterizing population-level
change. For example, if we’d like to know how the average rate at which
cumulative basal area per acre has changed in our population of
interest, we’d shoot for net growth. Here’s how we do it with
rFIA, where net growth is indicated by the argument
treeType = 'all':
# Net annual change
net <- vitalRates(db,
treeType = 'all', # "all" indicates net growth
grpBy = SITECLCD, # Grouping by site productivity class
bySpecies = TRUE, # also grouping by species
variance = TRUE)
net## # A tibble: 304 × 26
## YEAR SITECLCD SPCD COMMON_NAME SCIENTIFIC_NAME DIA_GROW BA_GROW NETVOL_GROW
## <dbl> <int> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2021 1 11 Pacific si… Abies amabilis 0.0594 0.00833 0.457
## 2 2021 1 17 grand fir Abies grandis -0.142 0.00197 0.673
## 3 2021 1 22 noble fir Abies procera 0.187 0.0225 0.994
## 4 2021 1 73 western la… Larix occident… 0.511 0.0222 0.377
## 5 2021 1 93 Engelmann … Picea engelman… 0.551 0.0164 0.0867
## 6 2021 1 98 Sitka spru… Picea sitchens… 0.156 0.0203 1.01
## 7 2021 1 108 lodgepole … Pinus contorta 0.316 0.0124 0.200
## 8 2021 1 119 western wh… Pinus monticola 0.581 0.0182 0.167
## 9 2021 1 122 ponderosa … Pinus ponderosa 0.09 0.0237 1.40
## 10 2021 1 202 Douglas-fir Pseudotsuga me… 0.397 0.0232 0.595
## # ℹ 294 more rows
## # ℹ 18 more variables: SAWVOL_GROW <dbl>, BIO_GROW <dbl>, BA_GROW_AC <dbl>,
## # NETVOL_GROW_AC <dbl>, SAWVOL_GROW_AC <dbl>, BIO_GROW_AC <dbl>,
## # DIA_GROW_VAR <dbl>, BA_GROW_VAR <dbl>, NETVOL_GROW_VAR <dbl>,
## # SAWVOL_GROW_VAR <dbl>, BIO_GROW_VAR <dbl>, BA_GROW_AC_VAR <dbl>,
## # NETVOL_GROW_AC_VAR <dbl>, SAWVOL_GROW_AC_VAR <dbl>, BIO_GROW_AC_VAR <dbl>,
## # nPlots_TREE <int>, nPlots_AREA <int>, N <int>If we are instead interested in characterizing the average annual
growth rate of individual trees, we’d most likely want to exclude stems
that died or recruited into the population between plot measurements. To
do this with rFIA, simply set treeType = 'live':
# Annual growth on live trees that remained live
live <- vitalRates(db,
treeType = 'live', # "live" excludes mortality and recruitment
grpBy = SITECLCD, # Grouping by site productivity class
bySpecies = TRUE, # also grouping by species
variance = TRUE)By default, vitalRates() will estimate average annual
DBH, basal area, biomass, and net volume growth rates for individual
stems, along with average annual basal area, biomass, and net
volume growth per acre. Here we’ll focus in on basal area, so
let’s simplify and pretty up our tables a bit:
# Net annual change
net <- net %>%
# Dropping unnecessary columns
select(SITECLCD, COMMON_NAME, SCIENTIFIC_NAME, SPCD,
BA_GROW, BA_GROW_AC, BA_GROW_VAR, BA_GROW_AC_VAR, nPlots_TREE, N) %>%
# Dropping rows with no live trees (growth was NA)
filter(!is.na(BA_GROW)) %>%
# Making SITECLCD more informative
mutate(site = case_when(SITECLCD == 1 ~ "225+ cubic feet/acre/year",
SITECLCD == 2 ~ "165-224 cubic feet/acre/year",
SITECLCD == 3 ~ "120-164 cubic feet/acre/year",
SITECLCD == 4 ~ "85-119 cubic feet/acre/year",
SITECLCD == 5 ~ "50-84 cubic feet/acre/year",
SITECLCD == 6 ~ "20-49 cubic feet/acre/year",
SITECLCD == 7 ~ "0-19 cubic feet/acre/year")) %>%
# Arrange it nicely
select(COMMON_NAME, SCIENTIFIC_NAME, SITECLCD, site, everything()) %>%
arrange(COMMON_NAME, SCIENTIFIC_NAME, SITECLCD, site)
# Annual growth on live trees that remained live
live <- live %>%
# Dropping unnecessary columns
select(SITECLCD, COMMON_NAME, SCIENTIFIC_NAME, SPCD,
BA_GROW, BA_GROW_AC, BA_GROW_VAR, BA_GROW_AC_VAR, nPlots_TREE, N) %>%
# Dropping rows with no live trees (growth was NA)
filter(!is.na(BA_GROW)) %>%
# Making SITECLCD more informative
mutate(siteProd = case_when(SITECLCD == 1 ~ "225+ cubic feet/acre/year",
SITECLCD == 2 ~ "165-224 cubic feet/acre/year",
SITECLCD == 3 ~ "120-164 cubic feet/acre/year",
SITECLCD == 4 ~ "85-119 cubic feet/acre/year",
SITECLCD == 5 ~ "50-84 cubic feet/acre/year",
SITECLCD == 6 ~ "20-49 cubic feet/acre/year",
SITECLCD == 7 ~ "0-19 cubic feet/acre/year")) %>%
# Arrange it nicely
select(COMMON_NAME, SCIENTIFIC_NAME, SITECLCD, siteProd, everything()) %>%
arrange(COMMON_NAME, SCIENTIFIC_NAME, SITECLCD, siteProd)Here BA_GROW gives us annual basal area growth per tree
in square feet/year, and BA_GROW_AC gives us average basal
area growth per acre in square feet/acre/year. But maybe we’d prefer
units to be square centimeters instead - just remember to multiply the
variance by the square of the conversion factor!
# Net annual change
net <- net %>%
# Convert to square centimeters instead of square feet
mutate(BA_GROW = BA_GROW * 929.03,
BA_GROW_AC = BA_GROW_AC * 929.03,
BA_GROW_VAR = BA_GROW_VAR * (929.03^2),
BA_GROW_AC_VAR = BA_GROW_AC_VAR * (929.03^2))
net## # A tibble: 304 × 11
## COMMON_NAME SCIENTIFIC_NAME SITECLCD site SPCD BA_GROW BA_GROW_AC
## <chr> <chr> <int> <chr> <dbl> <dbl> <dbl>
## 1 Alaska yellow-cedar Chamaecyparis n… 2 165-… 42 11.2 0.659
## 2 Alaska yellow-cedar Chamaecyparis n… 3 120-… 42 3.30 0.700
## 3 Alaska yellow-cedar Chamaecyparis n… 4 85-1… 42 4.57 3.18
## 4 Alaska yellow-cedar Chamaecyparis n… 5 50-8… 42 3.24 3.13
## 5 Alaska yellow-cedar Chamaecyparis n… 6 20-4… 42 1.45 1.35
## 6 Alaska yellow-cedar Chamaecyparis n… 7 0-19… 42 0.236 0.203
## 7 Brewer spruce Picea breweriana 6 20-4… 92 3.75 0.0309
## 8 California black oak Quercus kellogg… 2 165-… 818 0.797 0.0241
## 9 California black oak Quercus kellogg… 3 120-… 818 -1.14 -0.122
## 10 California black oak Quercus kellogg… 4 85-1… 818 -5.38 -3.39
## # ℹ 294 more rows
## # ℹ 4 more variables: BA_GROW_VAR <dbl>, BA_GROW_AC_VAR <dbl>,
## # nPlots_TREE <int>, N <int>
# Annual growth on live trees that remained live
live <- live %>%
# Convert to square centimeters instead of square feet
mutate(BA_GROW = BA_GROW * 929.03,
BA_GROW_AC = BA_GROW_AC * 929.03,
BA_GROW_VAR = BA_GROW_VAR * (929.03^2),
BA_GROW_AC_VAR = BA_GROW_AC_VAR * (929.03^2))
live## # A tibble: 288 × 11
## COMMON_NAME SCIENTIFIC_NAME SITECLCD siteProd SPCD BA_GROW BA_GROW_AC
## <chr> <chr> <int> <chr> <dbl> <dbl> <dbl>
## 1 Alaska yellow-ced… Chamaecyparis … 2 165-224… 42 12.8 0.504
## 2 Alaska yellow-ced… Chamaecyparis … 3 120-164… 42 11.0 1.62
## 3 Alaska yellow-ced… Chamaecyparis … 4 85-119 … 42 6.95 4.05
## 4 Alaska yellow-ced… Chamaecyparis … 5 50-84 c… 42 4.68 3.76
## 5 Alaska yellow-ced… Chamaecyparis … 6 20-49 c… 42 4.20 3.44
## 6 Alaska yellow-ced… Chamaecyparis … 7 0-19 cu… 42 6.36 4.61
## 7 Brewer spruce Picea breweria… 6 20-49 c… 92 3.75 0.0309
## 8 California black … Quercus kellog… 2 165-224… 818 11.1 0.137
## 9 California black … Quercus kellog… 3 120-164… 818 4.09 0.215
## 10 California black … Quercus kellog… 4 85-119 … 818 4.77 2.22
## # ℹ 278 more rows
## # ℹ 4 more variables: BA_GROW_VAR <dbl>, BA_GROW_AC_VAR <dbl>,
## # nPlots_TREE <int>, N <int>