Field-Based Response Data
We obtained ecosystem field data from plot surveys of forested sites following methods detailed in Keys et al. (2023). Surveys involved measuring biotic (e.g., species coverage) and abiotic (e.g., humus depth) forest ecosystem properties (Figure 1) (additional survey information in S1). Following surveys, we calculated several aggregate ecosystem properties from plot data, including biotic (e.g., productivity) and abiotic variables (e.g., brown carbon stocks) (Figure 1; S1). For each of our three models, we employed a response data selection workflow (see S1) to satisfied model assumptions and to account for data errors or survey biases (e.g., missing data attributes).
We generally sought to build parsimonious models incorporating constituent and aggregate ecosystem properties drawn from biotic and abiotic domains. In our ecosystem model (see S1), we strove to ensure the number of biotic and abiotic response variables were relatively similar. For example, representing biota at the species level (664 taxa) would have skewed the ratio of biotic to abiotic response variables in this model, because fewer abiotic variables were available in the plot database. This imbalance would unduly weight the influence of biotic variation on model outcomes. To help address this inequity, we represented biota with ten functional groups (see S1) adapted from the Canadian National Vegetation Classification (Baldwin et al. 2019). Lastly, different measurement units (e.g., percent cover for species, centimeters for humus depth) were employed for quantifying the relativeabundance of biotic and abiotic properties (see S1). Mixing these variables introduced dissimilar data structures in the response data pool, with a potential bearing on model outcomes. We applied a cube root transformation to a subset of response variables (see S1) (Cox 2011) to help resolve this issue.