Forests are complex systems that persist over long spans of time. The establishment, growth, and death of individual trees within forests are governed by many factors, not all of which are fully understood (especially over long time spans). The growth of individual trees together, each tree competing for resources and responding to environmental conditions, is a multifaceted web of interactions. When one factor changes, the effects ripple through the forest ecosystem in potentially unexpected ways. As our climate, an important factor regulating tree growth, warms at unprecedented rates in the Far North, scientists are working to understand how these changes will impact forests.
Merging simulation models, robust statistical methods, and ecological field data is a frontier for improving inference and forecasting. Simulation models translate all we know about the factors influencing tree growth over its lifetime—climate, topography, soil conditions, competition—into an interconnected web of equations. The models simulate the patterns of individual trees and then calculate how the aggregation of those individual patterns leads to emergent system properties. Simultaneously, modern statistical techniques, such as machine learning, are rapidly being developed to provide predictions using “big” data in real time. Computational simulation models alone are necessarily incomplete representations of ecosystem functioning, and machine-learning approaches alone may not provide insights into causal mechanisms. Similarly, ecological field data can never fully observe an ecological system. Combining these techniques to understand mechanisms and improve predictions about forest growth dynamics is a promising path forward to advancing ecological understanding.
Bringing these tools together is not always straightforward. In this 2021 paper in Ecology and Evolution, Raiho and others demonstrate how this can be done using pragmatic techniques. They use tree-ring basal area reconstructions in Denali National Park and Preserve to inform successional trajectories of the two most common tree species in the park (black spruce and white spruce) simulated by a forest model, University of Virginia Forest Model Enhanced (UVAFME). The basic principle underlying UVAFME is competition for light, water, and nutrients between individual trees. Changing age, size and number of individual trees then feeds back to the ecosystem states to alter the availability of light, water, and nutrients in the following years. Each year, individual tree growth is constrained by the most limiting of these factors, resulting in realistic forest growth dynamics over successional timescales (hundreds of years).