Latest Score:

1.8/5

in 2016

score trend is flat over time
Weight: 12%

Forest connectivity is the degree to which the distance between adjacent forest patches impedes or facilitates movement1. Forests with higher connectivity better allow for the exchange of water and nutrients, movement of wildlife, dispersal and genetic interchange between populations, and long distance range shifts of species, such as in response to climate change2,3. Maintaining connectivity becomes a higher priority as forests become more fragmented with urban expansion. Not only must the connections exist but they must be functional as well. Here, forest connectivity is measured using the “Contagion Index” calculated using FragStats software4. The current year is scored as the difference between the data minimum -10% of the range and the long-term mean.

1Taylor, P.D., Fahrig, L., Henein, K. and Merriam, G., 1993. Connectivity is a vital element of landscape structure. Oikos, pp.571-573.
2Code of Federal Regulations. 2011. Title 36, Section 219.19.
3Sorenson, E and Osborne, J. 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
4McGarigal, 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 Forest Connectivity 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 1-5)

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 ‘Contagion’ as the Landscape Aggregation metric to compute. 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

STRUCTURE INDICATORS