## Overview and Terminology

Metrics included in the Forest Indicators Dashboard were selected through an expert working group, as well as based on data availability and quality. We sought to include a limited number of datasets per category, while providing a robust assessment of forest health based on a suite of measurements.

Periodic values for each metric produce a current condition value for the most recent year of the data, such as the current forest growth rate or total annual precipitation. These values are translated into scores on a 1 to 5 scale to provide an estimate of whether things are getting ‘better’ or ‘worse’. To generate the current year’s score, this value is compared to a target value. For example, for Hardwood Regeneration, the current-year value is scored based on where it falls between the minimum and maximum hardwood regeneration values in the entire dataset, and the target is higher regeneration. Higher hardwood regeneration density in any year thus receives a higher score. Conversely, for maximum annual temperature, the current-year score is computed as the distance away from the long-term mean, as deviations away from historical patterns can negatively affect forests. The closer the value for a given year is to the long-term mean, the higher the score will be. More specific details on the target, maximum, and minimum values, as well as computational notes are included with each metric. For more information, refer to our comprehensive methodology.

## Important Notes

**For many metrics, the computation of annual scores is dynamic.** This means that as new data becomes available and is added to the FID, previously calculated annual scores may change. If the annual scores are computed relative to the long-term mean, this addition of a new year of data could change the long-term mean and thus, the resultant annual scores. Because the scores could change from year to year, we offer snapshots of the dashboard over time, allowing you to revisit previous versions of the dashboard.

**Computation of annual scores is often limited by data availability.** For some datasets, we may alter annual score computation to be more ecologically relevant. For example, we may change the annual score computation from a deviation from the long-term mean to a deviation from a baseline.

**We did not assess non-linear trends.** The significance of each long-term trend was tested using linear regression and a p-value threshold of 0.05.

The dropdown menu at the top of this tab provides detailed descriptions of our methodology used to process and analyze the datasets included in FID. For each, we describe the **data source**, the **data processing**, the **target value**, and **computation of the annual score**.

## Terminology

- Upper scoring bounds
- The maximum value used for computing the annual score based on background knowledge and expert input. For some datasets, the upper scoring boards aligns with a predefined target or value (e.g., Shannon-Weiner Diversity Index of 3.5), while for other datasets, it is a bounding value based on the data range (e.g., maximum value plus 10% of the range). See example below for more information.
- Lower scoring bounds
- The minimum value used for computing the annuals score based on background knowledge and expert input. For some datasets, the lower scoring boards aligns with a predefined target or value (e.g., ozone exposure of 0 ppm-hour), while for other datasets, it is a bounding value based on the data range (e.g., minimum value minus 10% of the range). See example below for more information.
- Target
- The target value for the dataset is based on background knowledge and expert input. For some datasets, it can be the same as the upper or lower scoring bounds, while for other datasets, it is the long-term mean value. See example below for more information.
- Minimum
- The minimum value in the dataset
- Maximum
- The maximum value in the dataset

## How Scores Can Change Over Time

In the example below, we present graphs of data (blue solid line) and the trend of these data (blue dotted line) for three different time periods (2004-2015, 2004-2016, and 2004-2017). For these data, the target was set to be the long-term mean (red line). Notice that as new data are added each year (moving from 2015-2017 in the figures below), the target dynamically changes. The upper and lower scoring bounds (grey lines) were set to be the minimum value in the dataset -10% of the range and the maximum value in the dataset +10% of the range, respectively. Like for the target, as new data are included each year, these bounding values also change. In the example below, values for 2016 and 2017 are higher than in the rest of the dataset, so that the upper scoring bound increases as these years of data are added.