Data Supplement
- Supplementary Material - Pollen data and pollen threshold analysis, model descriptions, climate variable analyses, parameter complexity, performance by model type, AIC table for climate vs. compositional novelty, sample size analysis, prevalence analysis, and model over-fitting.
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Article Figures & Data
Figures
- Figure 1.
Temperature change and model comparison scheme. Solid line shows oxygen isotope data from 21 000 years ago (kyr BP) to the present from the North Greenland Ice Core Project (GICC05) [25] with the 2005 Greenland Ice Core Chronology age model [26]. Dashed line is mean maximum temperature from climate simulations used in this analysis. The Bølling–Allerød (B-A) and Younger Dryas (YD) Chronozones are highlighted in grey. The time periods used to fit SDM and CLM models are indicated with shaded boxes/rectangles, while projected time periods are indicated with open ellipses. This is illustrated for the 10 kyr BP time period (shaded box with thickened outline and grey arrows indicating projected time periods). Data were pooled for 15 to 14 kyr BP, 17.5 to 15.5 kyr BP and 21 to 15.5 kyr BP when building models (shaded rectangles) and for 17.5 to 15.5 kyr BP, 21 to 15.5 and 21 to 18 kyr BP when projecting models (open rounded rectangles). The number of localities used to fit models is indicated above the grey box representing the model fitting time period.
- Figure 2.
Model performance as a function of climatic and compositional novelty. AUC scores (a,d), 1−Brier scores (b,e) and Bray–Curtis similarity (1 – Bray–Curtis dissimilarity) (c,f) as a function of climatic novelty (a–c) and compositional novelty (d–f), fitted with a local polynomial regression (LOESS) line. Black circles and solid black lines represent SDM values. Grey circles and dashed lines represent CLM values. Circle size reflects sample size. (g) (inset in (a)) Compositional novelty as a function of climatic novelty.
- Figure 3.
Mean SDM and CLM performance. Mean AUC scores (top row), 1−Brier scores (middle row) and Bray–Curtis similarity (bottom row) of SDMs (left column) and CLMs (middle column) for each combination of fit in and projected to periods. Each square represents the average of all five modelling algorithms for all genera. Green shading indicates high performance and orange shading indicates low performance. The right column shows the differences between SDM and CLM scores across all five modelling algorithms and all genera. Blue shading indicates superior SDM performance while red shading indicates better CLM performance. Cells with black outlines indicate statistically significant differences between SDMs and CLMs based on a Wilcoxon test on the mean values for each metric. The thick solid black lines indicate models that were fit and projected to the same time period and thus divide hindcasted models (above line) from forecasted models (below line).
- Figure 4.
Number of localities and performance. Performance of models based on AUC scores (top row), 1 − Brier scores (middle row) and Bray–Curtis similarity (bottom row) when the number of localities used to fit the model in 1 kyr BP is the original number (n = 276) and subsampled to 100, 150 and 200 localities and then projected to chosen time periods. Solid line represents the mean value of each metric across all SDMs and the dashed line represents the mean across all CLMs.
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Volume 283, issue 1826
