DOI: 10.5061/dryad.gmsbcc31s
title: mapMOG: Assessing Mature and old growth forest using FIA data

description: Although many species display relationships – both positive and negative –
 with various forest successional stages, researchers often rely on
 datasets that describe forest presence rather than forest age (i.e.,
 National Landcover Database). In 2023, the USDA Forest Service introduced
 standardized region-specific mature and old-growth (MOG) forest
 definitions for the United States, but these definitions have not been
 readily integrated to address questions in ecology and conservation. Here,
 we introduce ‘mapMOG’—an open-access R function that applies the recently
 adopted federal MOG definitions to Forest Inventory and Analysis (FIA)
 plots across the contiguous United States (US). Additionally, our novel
 function interpolates MOG status across US forested lands between FIA
 plots. To demonstrate the utility of these data for forest landscape
 ecological modeling, we compare the predictive power of the MOG covariate
 against binary forest/non-forest and percent canopy cover covariates to
 examine forest habitat associations across three taxa, geographies, and
 modelling frameworks: avian richness in Mid-Atlantic national parks;
 Seminole bat (Lasiurus seminolus) occupancy in the Southeastern Coastal
 Plain; and Cascade torrent salamander (Rhyacotriton cascadae) distribution
 associated with US National Forests in the Pacific Northwest. In all three
 cases, our MOG covariate produced by our function explained variation in
 the wildlife occurrence data better than the alternative forest metrics.
 Finally, we compared imputed results across multiple spatial scales and
 found notable but statistically insignificant differences in interpolated
 MOG scores.
description: # mapMOG: Assessing Mature and old growth forest using FIA data Dataset
 DOI: [10.5061/dryad.gmsbcc31s](10.5061/dryad.gmsbcc31s) ## Description of
 the data and file structure **Assessing Mature and Old Growth Forest using
 FIA data** Mature and old growth forest support vast quantities of life,
 and are critical to the function of many ecosystems. While remotely sensed
 landcover datasets can indicate the spatial distribution of forests, they
 offer little insight to the age of forests. Fortunately, the United States
 Forest Service maintains an extensive forest inventory program across
 public and private lands called the [Forest Inventory
 Analysis](https://research.fs.usda.gov/programs/fia) (FIA). This function
 uses R (specifically the [RFIA package](https://doserlab.com/files/rfia/))
 to access FIA data from the Forest Service data repository, then assesses
 the data to determine if it meets the definitions for
 [mature](https://www.sciencedirect.com/science/article/abs/pii/S0378112723005959) or [old growth](https://www.sciencedirect.com/science/article/abs/pii/S0378112723006710) forest. Definitions vary by region and forest-type, offering substantial flexibility even across ecosystems. Finally, this function offers the option to interpolate the mature/old growth (MOG) status of unsampled forests based on neighboring sampled forests. The following tutorial explains how to use the function. ### Files and variables #### File: mapMOG.zip **mapMOG tutorial.pdf** is the fully annotated tutorial of how to access and use the mapMOG function in full detail. **Subfolder: tutorial_Olympic** OlympicNF: a shapefile of a (contiguous) selection of Olympic National Forest **Subfolder: utility_canopy** (this folder is intentionally empty, users should place the most recent NLCD canopy cover raster in this folder) **Subfolder: utility_ecoregions** USA_ecoregions: A shapefile (polygon) of Level 3 ecoregions of the United States of America **Subfolder: utility_MTcontDivide** utility_MTcontDivide: A shapefile (polygon) indicating the regions east of the continental divide in the state of Montana **Subfolder: utility_NWFPboundary** NWFPboundary: A shapefile (polygon) of the boundary of the areas managed under the Northwest Forest Plan, covering the western portions of Washington, Oregon, and California **Subfolder: utility_ORcounties** counties: A shapefile (polygon) of Hood River County and Wasco County, Oregon **Subfolder: utility_USA** tl_2020_us_state: A shapefile (polygon) of the contiguous United States **R code: FUNCTION_mapMOG:** script containing the mapMOG function as well as the tutorial function **Spreadsheet: utility.v9-5_2024-10_Natl_MasterTreeSpeciesList:** List of tree species and the species codes used by the United States Forest Service (columns listed below) * *FIA Code*: Species-specific numeric code used in Forest Inventory Analysis * *Common Name*: Common name for plant species in English * *Genus*: Genus name for plant species * *Species*: Species name for plant species * *Subspecies*: If applicable, subspecies epithet for species * *PLANTS Code*: Species-specific 4-6 character code using letters and numbers, recognized by the USDA plants database * *SCIENTIFIC_NAME*: Complete species-specific name in binomial nomenclature  **Raster: utility_R5_PAZ:** A raster of plant association zones (i.e., vegetative community types) in region 5 (Pacific Southwest). Identifying a study area The `mapMOG()` function requires the user to identify a study area using a shapefile in `sf` format. The study area, called a `locale`, can be as large as a state, or as small as a forest. Please note, however, that the function cannot currently accommodate multi-state processing. If your study `locale` spans multiple states, the function must be run separately for each state contained in the `locale`. Additionally, small forests are not likely to contain enough FIA plots for adequate interpolation. Assessing data at the landscape-level (e.g., county, etc.) is likely to produce greater insights, even if you are only interested in a small portion of the landscape. In this tutorial we will use part of Olympic National Forest as a case study. A shapefile of Olympic National Forest is included in the function folder for your convenience. ## Downloading and Accessing the function and data Download the entire mapMOG folder from Dryad and unzip the folder if necessary. We recommend moving the folder from the Downloads folder of your computer to a more permanent location. **Do not remove files from the** **`mapMOG`** **folder, as these files are necessary for proper functionality.** ## Code/software File size limitations barred us from including all necessary files in the ‘mapMOG’ folder available for download. Prior to running the function, download the following files and add them to the mapMOG folder: *If you plan on interpolating MOG across unsampled landscapes* 1. Download the most recent canopy cover layer from the National Land cover Database: [https://www.mrlc.gov/data](https://www.mrlc.gov/data) 2. Unzip the downloaded folder into the ‘utility_canopy’ subfolder of ‘mapMOG’ such that the file structure is ‘[YOUR FILE PATH]/mapMOG/utility_canopy/[CANOPY RASTER]’ 3. No need to rename the raster Once you have added the canopy and PNV files to your `mapMOG` folder (if necessary), open R Studio and load the function by sourcing the script from the `mapMOG` folder. Note that the entire filepath must be specified for your computer to find the file. Dynamic data exploration can be achieved using the `mapview` package: `mapview(MOG.raster.transformed)` If you encounter issues with the `mapMOG()` function, please email Dan Herrera at *herrerawildlife(at)gmail.com*. ## Access information Other publicly accessible locations of the data: * [https://github.com/Dan-Herrera/Mature-and-Old-Growth-Forest-Assessment](https://github.com/Dan-Herrera/Mature-and-Old-Growth-Forest-Assessment) Data was derived from the following sources: * [https://doserlab.com/files/rfia/](https://doserlab.com/files/rfia/) * [https://research.fs.usda.gov/programs/fia](https://research.fs.usda.gov/programs/fia)