Differing climatic mechanisms control transient and accumulated vegetation novelty in Europe and eastern North America

Understanding the mechanisms that produce novel ecosystems is of joint interest to conservation biologists and paleoecologists. Here, we define and differentiate transient from accumulated novelty and evaluate four climatic mechanisms proposed to cause species to reshuffle into novel assemblages: high climatic novelty, high spatial rates of change (displacement), high variance among displacement rates for individual climate variables, and divergence among displacement vector bearings. We use climate simulations to quantify climate novelty, displacement, and divergence across Europe and eastern North America (ENA) from the last glacial maximum (LGM) to present and fossil pollen records to quantify vegetation novelty. Transient climate novelty is consistently the strongest predictor of transient vegetation novelty, while displacement rates (mean and variance) are equally important in Europe. However, transient vegetation novelty is lower in Europe and its relationship to climatic predictors is the opposite of expectation. For both continents, accumulated novelty is greater than transient novelty, and climate novelty is the strongest predictor of accumulated ecological novelty. These results suggest that controls on novel ecosystems vary with timescale and among continents, and that the 21st-century emergence of novel ecosystems will be driven by both rapid rates of climate change and the emergence of novel climate states.

Methods
To quantify vegetation change and novelty across Europe and eastern North America, we use a selection of sites with standardized age models. Pollen counts are aggregated to 43 types, and are collated into 43 consecutive 500-year-wide age bins, centred on 500-year intervals, from 21 to 0 ka BP.

All paleoclimate simulations are from the CCSM3 SynTrace experiments, bias-corrected and downscaled to 0.5° x 0.5° following the methods described by Lorenz et al. (2016).

We modelled the predictors of vegetation novelty for the full spatio-temporal dataset, using a linear mixed effects model with the nlme and MuMIn packages in R.

Relative variable importance (RVIv) is used to determine the strength of support for a given variable v, as a predictor of vegetation novelty. RVIv is determined by summing the Akaike weights of each variable across all candidate models in which a given variable v, occurs.