trend is
flat
over time
Forest cover is the percent of the state of Vermont with tree cover.
trend is
down
over time
Regeneration of sugar maple seedlings provides information about the future of Vermont's hardwood forests.
trend is
flat
over time
Regeneration of red spruce seedlings provides information about the future of Vermont's softwood forests.
trend is
up
over time
Forests with greater stand complexity have trees in a range of sizes and as a result, may be more productive and resilient to stress.
trend is
flat
over time
Forest patch sizes provides information on the average size of contiguous forest blocks.
trend is
flat
over time
Forest connectivity is a measure of the linkages among Vermont's forests.
trend is
flat
over time
With greater diversity in tree species, forests can support more biodiversity, exhibit higher resilience to stress, and store more carbon.
trend is
down
over time
Across the landscape, having a range of forest stand ages provides diversity, varied habitat conditions, and resilience to stressors.
Latest Score:
4.7/5
in 2019
Stand complexity is the proportion of trees in a set of diameter classes. Forest stands can be composed of trees in a variety of sizes, and stands with greater complexity in tree sizes are often more resilient to disturbances and other stressors, and more productive^{1}. Complex stands can also provide more varied habitat for wildlife and sustain higher biological diversity. Here, stand complexity is assessed using the Shannon-Weiner Index, using the proportion of trees per 5" diameter size class. For a high score, the forest would have to have an equal number of trees in each size class represented.
The score is calculated using a target value and the historical range of the the entire long-term dataset. The higher the score, the closer this year's value is to the target.
Once the score is computed for each year, the trend in scores over time is calculated. If the trend is significantly positive or negative, the long-term trend is marked as increasing or decreasing respectively.
Component | Description |
---|---|
Scored as | Distance between minimum and maximum (scaled 1-5) |
Target value | Data maximum + 10% of the range |
Directionality of scores | Higher values in the data are better |
Minimum value used in scoring | Data minimum -10% of the range |
Maximum value used in scoring | Data maximum + 10% of the range |
Using the USFS Forest Inventory and Analysis Program population estimate data on Phase 2 plots accessed via the FIA Datamart^{1}, we computed tree size class diversity using a Shannon-Weiner Diversity Index calculation (see equation below). The first available data year was 1997. We divided all sampled trees into 5 inch classes and tallied the total proportion of each size class measured in the plots. To compute the annual score, we used FIA’s plot evaluation group panels to create population estimates for each year. The target for this dataset was set as the maximum value plus 10% of the range. The target was set as the upper scoring bounds (dataset maximum plus 10% of the range), and the current year is scored for where it falls between the lower scoring bounds (dataset minimum minus 10% of the range) and the target, scaled to be between 1 and 5.
Shannon-Weiner Index (H’)
Where p_{i} is the proportion of trees in the ith size class
Dataset: VT Plot Snapshot Table
Dataset: VT Condition Table
Dataset: VT Population Evaluation Group Table
Dataset: VT Tree Table