Abstract: The Experimental Forest Research Network (EFRN) is an existing ecological research network that allows for long-term monitoring and experimentation. Such a network aims to generate knowledge that can be scaled across the broader landscape. The goal of this research is to assess how well the existing network does this and identify prospective network additions that would optimize the representativeness of the network. With the existing 73 experimental forest sites across the contiguous United States, we can assess how well the biophysical conditions at those sites represent the broader forested landscape. To do this, we utilize multivariate analysis of environmental variables to quantitatively map both how well represented any given forested area is (representativeness) and which experimental forest best represents that area (constituency). Representativeness of the EFRN was good across most of the US, with areas such as Florida, Texas, and portions of the Pacific NorthWest being the exception. In measuring the change in representativeness across the landscape with the addition of each experimental forest, we see that some additions contribute greatly, while others do very little to expand the network's reach. With these results, we identify where the contiguous US has worse than average representativeness and use another multidimensional ordination of those areas to generate a map of relative contribution to increasing representativeness. These analyses are intended to allow decision-makers to consider network optimization in their plans for future growth of the network, and also provide science users with a framework to select relevant observations and scale to the broader landscape.
