Between forests and trees: Building demographic models for global change research
Models of forests in global change research strive to integrate the effects of climate on many species in order to simulate a complex system over large spatial and temporal domains. Any analysis of the demography of tree species, however, can readily demonstrate the difficulty in assessing even straightforward summaries of vital rates for common species, each of which can demonstrate very different life histories. Many commonly used demographic models, such as integral projection models (IPMs), build population-level inference by combining vital rate models of individual observations over time. For forests, where the individuals (trees) have highly skewed size distributions, and yet grow in small increments and survive at high rates throughout their life-cycle, moving from individual observation through survival and growth models to population projections can face some important challenges. Inference about communities is further complicated by the disproportionate number of rare species. Different ways of including information across species (such as through functional traits) have been posited, but require a general framework in order to be tested.
The workflow we have developed demonstrates how using inventory data can connect the individual tree scale to inference about forests through process models and inverse estimation across several scales of information. This is a flexible statistical modeling platform, and explores of the boundary between IPMs and individual based models (IBMs) with implications for computational efficiency in extrapolating population-scale demography to regional patterns. An application of this model uses data from a forest plot in the Democratic Republic of Congo to show species-specific population response to a monodominant tree with implications for foreset biomass and carbon cycling.