Latest Score:

2/5

in 2016

score trend is flat over time
Weight: 12%

When large forest blocks are subdivided during development, the average forest patch size decreases. As these forests become smaller, they are less supportive to wildlife and limit the dispersal of both plants and animals1. Because subdivision creates more forest edge, these smaller patches may have a higher concentration of invasive plants and may be more prone in insect and disease invasions. Both the ecosystem services (such as recreational opportunities, moderation of fluctuating temperatures, and water filtration) and timber-based economic viability of these smaller patches are reduced. Here, we use remotely-sensed satellite data to assess the average size of forest patches in Vermont using FragStats software2. The current year is scored as the distance between the data minimum -10% of the range and the long-term mean.

1 Sorenson, E and J Osborne. 2014. Vermont habitat blocks and habitat connectivity: an analysis using geographic information systems. Available at: http://www.vtfishandwildlife.com/UserFiles/Servers/Server_73079/File/Conserve/Vermont_Habitat_Blocks_and_Habitat_Connectivity.pdf
2 McGarigal, K., SA Cushman, and E Ene. 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.html

-- Expert interpretation for Mean Forest Patch Size is not available--

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 target (scaled between 1 and 5)

Target value

Long-term mean

Directionality of scores

No change from the long-term mean is better

Minimum value used in scoring

Data minimum - 10% of the range

Maximum value used in scoring

Data maximum + 10% of the range

From the Forest Cover images we generated (see details under that metric above) .While the first available year of data was 1992, NLCD methodology changed between 1992 and 2006, so we began our analysis with 2001 data. We used FragStats1 to compute the mean size of forest patches. We used the 8 cell neighborhood rule with a ‘no sampling’ strategy. We selected all Area-Edge Class metrics for computation. We set the target for this dataset to be the long-term mean. The current year is scored for where it falls between target and either the lower scoring bounds (dataset minimum minus 10% of the range) or the upper scoring bounds (dataset maximum plus 10% of the range) and the target when values are above the target, scaled to be between 1 and 5.

1 McGarigal, K., SA Cushman, and E Ene. 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.html

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