Abstract
The growth of forests and the feedbacks between forests and environmental changes are central issues in the planetary carbon cycle, global climate change, and basic plant ecology. A challenge to understanding both growth and feedbacks from local to global scales is that many critical metabolic processes vary among species. An innovation in solving this challenge is the recognition that species can be lumped into “functional groups” based on metabolic similarity, and these functional groups can then be studied in computational models that simulate ecosystem function. Despite the vast resources devoted to functional group studies and the progress made by them, an important logical and biological question has not been formally addressed, “How do the groupings alter the results of modeling studies?” To what extent do modeling results depend on the choices made in aggregating taxa into functional groups. Here, we consider the effects of using different aggregation strategies in simulating the carbon dynamics of a deciduous forest. Understanding the impacts that aggregation strategy has on efforts to simulate regional-to-global-scale forest dynamics offers insights into both ecosystem regulation and model function and addresses this central problem in the study of carbon dynamics.
Highlights
We apply a well-established individual-based forest model to investigate theoretical issues involving the consequences of grouping data from different species of trees into functional groupings or plant functional types (PFTs)
We examine multiple aggregation strategies and the impacts of using different points in succession to develop weightings on model output, and we place these results in the context of the uncertainties associated with remotely sensed data that are often used to calibrate/validate ecosystem models
The groups in the four lumping protocols show a clear increase in biomass averaged across the species in each functional group (Figure 1b)
Summary
We apply a well-established individual-based forest model to investigate theoretical issues involving the consequences of grouping data from different species of trees into functional groupings or plant functional types (PFTs). Such groupings are often essential to the initial construction of simulation models of forest ecosystems over time [1,2]. As discussed in detail below, species richness militates an enormous amount of specific empirical knowledge. This can produce immense model complexity in models of diverse ecosystems. Upscaling biophysical and physiological mechanisms is bedeviled by the problem that there is no coherent theoretical basis for treating a multi-species canopy as having system-level optimization properties, as there is for single plants [5]
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