%0 DATA
%A Anieke, Brombacher
%A Paul, A. Wilson
%A Ian, Bailey
%A Thomas H. G., Ezard
%D 2018
%T Supplementary figures S1-S5 from Temperature is a poor proxy for synergistic climate forcing of plankton evolution
%U https://rs.figshare.com/articles/journal_contribution/Supplementary_figures_S1-S5_from_Temperature_is_a_poor_proxy_for_synergistic_climate_forcing_of_plankton_evolution/6743357
%R 10.6084/m9.figshare.6743357.v1
%2 https://rs.figshare.com/ndownloader/files/12302402
%K microevolution
%K temperature
%K CO2
%K productivity
%K foraminifera
%K abundance
%X S1: Study species Globoconella puncticulata (a) and Truncorotalia crassaformis (b). The scale bars represent 100 μm. S2: Map of study site IODP Site U1313 in the North Atlantic. S3: Autocorrelation plots for traits of Globoconella puncticulata showing autocorrelation in the original time series (a-c) but not in the first differences (d-f). S4: Autocorrelation plots for traits of Truncorotalia crassaformis showing autocorrelation in the original time series (a-c) but not in the first differences (d-f ). Only area in T. crassaformis still contained a small amount of autocorrelation in the first differences time series. However, comparison of two Generalized Least Squares models with and without autocorrelation (by setting the continuous auto-correlation parameter phi=0.3 and phi=0.1, which converges to 0 under maximum likelihood, respectively) show lower AIC values for the model without autocorrelation, implying that the model with temporal autocorrelation is actually a poorer fit to the data than that without. S5: ΔAIC values showing the added relative importance of each environmental parameter or combination of parameters to the total model.