A handy R function for getting ecological division from FIA data

November 18, 2023
data management forest carbon forest inventory and analysis forest inventory ecodivision

Ecological sections and subsections in Maine, USA (from Cleland et al. 2007).
Ecological sections and subsections in Maine, USA (from Cleland et al. 2007).

To get a better feel for how they work, I’ve been digging into the new tree volume, biomass, and carbon models released by the USDA Forest Service last month. These new equations replace volume equations developed at the regional level and are now nationally-consistent. Importantly for anyone that works with forest carbon inventory data, these new equations replace the Component Ratio Method (CRM).

Although the equations replace regional ones previously implemented, they do require a geographical variable not widely used in forest biomass and carbon equations: ecological division. These divisions are outlined in the US National Hierarchical Framework of Ecological Units, described in detail in a map produced by Cleland and others in 2007. Within divisions there are ecological provinces. Within those provinces there are ecological sections and subsections.

If you’re doing an analysis on an individual ownership, you can simply look up the property’s ecological division and find the equations for the tree species that you need. But if you’re doing an analysis at a larger scale, e.g., across a US state, there may exist multiple ecological divisions within your project area. Ecological divisions don’t line up with regional, state, or county boundaries, making it tricky to keep track of which volume, biomass, and carbon equation to use. For example, a state like Maine has three different ecological divisions. Hence, the same-sized eastern white pine tree located in three different locations in the state could have three different estates of tree biomass.

This issue will arise for analysts that do statewide assessments using Forest Inventory and Analysis (FIA) data. While the FIA database has been updated with the new estimates of tree volume, biomass, and carbon, analysts often will need to use the FIA data and write customized functions based on the data.

Fortunately, the PLOT table in the FIA database contains the ecological subsection where the plot resides, stored in the ECOSUBCD variable. As described in the FIA Database User Guide, ecological subsections are defined as areas of “similar surficial geology, lithology, geomorphic process, soil groups, subregional climate, and potential natural communities.” This represents the most detailed level within the hierarchical framework.

You’ll need to do some work to pull the ecological division out from this variable, and it’s messy because of the alpha-numeric characteristics and differing lengths of of the ECOSUBCD variable. For example, ECOSUBCD 211Da in central Maine is termed the Central Maine Embayment Subsection.

I’ll use some functions from the stringr package in R to do this, which can be called from the tidyverse library:

library(tidyverse)

First, I’ll make a data table called eco_codes of all of the ECOSUBCD values list in Maine’s PLOT table from FIA’s ME_PLOT.csv file using the as_tibble() function. Then I’ll rename the variables to something that makes sense. You can see that Maine has 19 unique ecological subsections:

eco_codes <- as_tibble(table(me_plot$ECOSUBCD), .name_repair = "unique") |> 
  rename(ECOSUBCD = ...1,
         num_plots = n)

eco_codes
## # A tibble: 19 × 2
##    ECOSUBCD num_plots
##    <chr>        <int>
##  1 211Aa         1499
##  2 211Ab          776
##  3 211Ba          962
##  4 211Bb         2020
##  5 211Ca          788
##  6 211Cb          820
##  7 211Da         2697
##  8 211Db          434
##  9 211Dc          628
## 10 221Ai          162
## 11 221Ak          425
## 12 221Al          785
## 13 M211Aa         952
## 14 M211Ab        2386
## 15 M211Ac        1546
## 16 M211Ad         103
## 17 M211Ae         648
## 18 M211Af        1088
## 19 M211Ag         767

We can determine the ecological province (eco_province) by using the str_sub() function by “trimming off” the subsection letters at the end of the subsection name. This can be done by specifying end = -3 in the function.

If only it were that simple. To obtain the ecological division we need to “round down” the ecological province. For example, Ecological province 211 (Northeastern Mixed Forest Province) corresponds to ecological division 210 (Warm Continental Division). To do this you can replace the last character in the eco_province variable with a zero. This can be done with the str_replace() function and replacing the last character using the ".$" command.

In the end, you can see the relationships between the ecological subsection, province, and division codes:

eco_codes <- eco_codes |> 
  mutate(eco_province = str_sub(ECOSUBCD, end = -3),
         eco_division = str_replace(eco_province, ".$", "0"))
eco_codes
## # A tibble: 19 × 4
##    ECOSUBCD num_plots eco_province eco_division
##    <chr>        <int> <chr>        <chr>       
##  1 211Aa         1499 211          210         
##  2 211Ab          776 211          210         
##  3 211Ba          962 211          210         
##  4 211Bb         2020 211          210         
##  5 211Ca          788 211          210         
##  6 211Cb          820 211          210         
##  7 211Da         2697 211          210         
##  8 211Db          434 211          210         
##  9 211Dc          628 211          210         
## 10 221Ai          162 221          220         
## 11 221Ak          425 221          220         
## 12 221Al          785 221          220         
## 13 M211Aa         952 M211         M210        
## 14 M211Ab        2386 M211         M210        
## 15 M211Ac        1546 M211         M210        
## 16 M211Ad         103 M211         M210        
## 17 M211Ae         648 M211         M210        
## 18 M211Af        1088 M211         M210        
## 19 M211Ag         767 M211         M210

You can see that most FIA plots in Maine are found in the Division 210 (Warm Continental Division), followed by M210 (Warm Continental Division - Mountain Provinces) and 220 (Hot Continental Division):

eco_codes |> 
  summarize(num_plots_division = sum(num_plots),
            .by = "eco_division")
## # A tibble: 3 × 2
##   eco_division num_plots_division
##   <chr>                     <int>
## 1 210                       10624
## 2 220                        1372
## 3 M210                       7490

Surely, there must exist an ecological subsection lookup table that contains each subsection and its corresponding values for ecological sections, divisions, and so forth. I haven’t been able to find one yet, but if you’re aware of one, do let me know! For know I’ll likely continue use functions like this to pull out these values.

By Matt Russell. Email Matt with any questions or comments.

Preliminary thoughts on using the new National Scale Volume and Biomass Estimators

February 26, 2024
forest inventory and analysis forest carbon forest inventory forest measurements NSVB FIA analytics

A quick and easy way to estimate forestland area change across US states

February 16, 2024
forest inventory forest ownership forest inventory and analysis forestland area FIA

How much does adding previous diameter and height growth change FVS predictions?

October 31, 2023
analytics carbon forest carbon forest inventory forest inventory and analysis forest measurements FVS growth and yield