Biotic interactions moderate the climate-vegetation relationship over the last 2,000 years of the pre-Industrial Holocene in the Upper Midwest, U.S.
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Forest aboveground biomass comprises a substantial proportion of terrestrial carbon storage. Understanding the drivers of forest aboveground biomass change, especially in the context of global climate change, is critical for making informed policy and management decisions. Change in forest aboveground biomass is driven by a combination of environmental variables and biotic interactions (e.g., competition, facilitation). Because of the long lifespans of forest trees, the contributions of climate drivers and biotic interactions to changes in forest aboveground biomass only become fully apparent over long time periods (decades to centuries). As a consequence of the lower availability of time series data spanning decades to centuries, processes occurring over long time spans are often overlooked. Understanding the degree to which biotic interactions moderate the relationship between climate and forest aboveground biomass is crucial to informing process models making long-term forecasts of the carbon cycle.
Our objective was to estimate the relationship between climate drivers and forest community composition, a predictor of forest aboveground biomass, over the last 2,000 years of the Holocene in the Upper Midwest, U.S. We quantified the relationship between average temperature and precipitation and forest community composition, estimated from fossil pollen, via a Bayesian generalized linear regression model. We used latent species covariance as an indicator of the magnitude of biotic effects on community composition after accounting for the effects of climate. We found that on average, temperature and precipitation explained approximately 30% of variation in the fraction of the landscape occupied by a given taxon. After accounting for temperature and precipitation, there were residual correlations between taxa in our study region. For example, we observed persistent negative correlations between the oak taxon and nearly every other taxon. Our results imply that at large spatial and temporal scales, changes in vegetation composition, and consequent changes in aboveground biomass, are driven by both climate drivers and biotic interactions. Ecological forecasting should consider integrating long-term data with predictive models to improve forecasts over decadal to centennial time scales.