An empirical, hierarchical typology of tree species assemblages for assessing forest dynamics under global change scenarios

Abstract

The composition of tree species occurring in a forest is important and can be affected by global change drivers such as climate change. To inform assessment and projection of global change impacts at broad extents, we used hierarchical cluster analysis and over 120,000 recent forest inventory plots to empirically define forest tree assemblages across the U.S., and identified the indicator and dominant species associated with each. Cluster typologies in two levels of a hierarchy of forest assemblages, with 29 and 147 groups respectively, were supported by diagnostic criteria. Groups in these two levels of the hierarchy were labeled based on the top indicator species in each, and ranged widely in size. For example, in the 29-cluster typology, the sugar maple-red maple assemblage contained the largest number of plots (30,068), while the butternut-sweet birch and sourwood-scarlet oak assemblages were both smallest (6 plots each). We provide a case-study demonstration of the utility of the typology for informing forest climate change impact assessment. For five assemblages in the 29-cluster typology, we used existing projections of changes in importance value (IV) for the dominant species under one low and one high climate change scenario to assess impacts to the assemblages. Results ranged widely for each scenario by the end of the century, with each showing an average decrease in IV for dominant species in some assemblages, including the balsam fir-quaking aspen assemblage, and an average increase for others, like the green ash-American elm assemblage. Future work should assess adaptive capacity of these forest assemblages and investigate local population- and community-level dynamics in places where dominant species may be impacted. This typology will be ideal for monitoring, assessing, and projecting changes to forest communities within the emerging framework of macrosystems ecology, which emphasizes hierarchies and broad extents.

Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

“…The most rare species (those occurring on less than 250 plots) were excluded to avoid biasing the analysis toward extremely rare species (McCune and Grace 2002), and nonnative species were excluded because those species may respond to different environmental drivers than native species. We used the same data set of relative importance by tree species for the comparisons among classifications as well as indicator and dominant species analysis described below (see Costanza et al 2017 for a list of all common and scientific names of tree species used here).…”

“…The third classification of FIA plots was a recently developed set of empirically-derived forest community types (Costanza et al 2017). The empirical classification used an unsupervised hierarchical method to cluster tree species composition information within FIA inventory plots across the conterminous U.S. (Costanza et al 2017).…”

“…Supervised approaches are often done using a decision rule or expert knowledge. In contrast, in a purely unsupervised, empirical, or "data-driven" classification, inventory data are partitioned into community type classes based on the relative similarity of their plot characteristics without regard to any published authority (Costanza et al 2017). Because no classification of forest community types will be suitable for all research questions, it is important to understand the strengths and weaknesses of each.…”

“…The USNVC classes were applied to FIA plots using a supervised key developed by FIA and NatureServe staff (Menard et al 2017). The empirical classes are the result of an unsupervised cluster analysis of all tree species in FIA plots (Costanza et al 2017).…”

“…Understanding how these approaches lead to differences or similarities in the resulting classifications will be critical for those who wish to use these classifications to examine forest-related questions. In addition, each of these classifications have been used already to study recent and potential future changes to forest communities (e.g., Iverson and Prasad 2001;Palmquist et al 2014;Costanza et al 2017) and each has the potential to support additional research on forest change. While an assessment of the performance of these classifications in models of forest change was beyond the scope of this paper, we here examine how well the classifications corresponded with local and broadscale environmental variables.…”