DOI: 10.5061/dryad.9w0vt4b9s
title: Data from: Differing climatic mechanisms control transient and accumulated vegetation novelty in Europe and eastern North America

description: 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.
description: 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
 <em>nlme</em> and <em>MuMIn</em> packages in
 R. Relative variable importance
 (<em>RVI<sub>v</sub></em>) is used to determine
 the strength of support for a given variable <em>v</em>, as a
 predictor of vegetation novelty.
 <em>RVI<sub>v</sub></em> is determined by summing
 the Akaike weights of each variable across all candidate models in which a
 given variable <em>v</em>, occurs.
description: Data repository associated with: 'Differing climatic
 mechanisms control transient and accumulated vegetation novelty in Europe
 and eastern North America' Burke, KD, Williams,
 JW, Brewer, S, Finsinger, W, Giesecke, T, Lorenz, DJ, Ordonez, A
 Files included: The input data for
 the linear mixed effects modelling (LMM) analyses. Each file contains the
 vegetation dissimilarity values, binned to 500-year intervals, in
 conjunction with the gridded climatic mechanism data. Seasonal climate
 data associated with the local sample are also included.
 The fitted coefficients of all candidate mixed effects models
 and the Relative Variable Importance (Akaike weights) of each variable, by
 region.  Downscaled, bias-corrected climate
 simulations from the CCSM3 SynTrace experiments, following the methods of
 Lorenz et al. (2016), Scientific Data (see doi:10.1038/sdata.2016.48 for
 North America results).