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Data were selected using the same criteria as for the SIFM model (Olsen et al.
Observatory data were selected using the same criteria as for the satellite data and were not further decimated.
The synthetic data were selected using the following criteria: K p ≤ 2 o, −20 nT ≤ Dst ≤ 20 nT, −8 nT ≤+ IMF B y ≤ 8 nT and −2 nT ≤ IMF B z ≤ 6 nT.
Models are nucleotide and amino acid data were selected using ModelGenerator.
Then, the best-suited nucleotide substitution models for these data were selected using Akaike information criterion (AIC) in Modeltest3.6 (Posada and Crandall 1998).
Once all the data were collected and assembled into a spreadsheet, leaf temperatures and all associated data were selected using a sorting procedure with SAS 9.13 (SAS Institute, Inc., Cary, NC, USA) into 5 ± 2.5 °C bands from 20 to 45 °C.
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Level 1b magnetic data are selected using standard geomagnetic indices: Kp, Dst and the interplanetary magnetic field (IMF) By and Bz components.
Linear, polynomial and logarithmic regressions were fitted to the data, and the significant regression that best-fit the data was selected using Akaike's Information Criterion.
For the remaining five analyses the model of protein evolution that best fits the data was selected using MEGA.
The value of K that best fits our data was selected using the Δ K statistic [ 37].
The statistically significant model for the data was selected using a series of LRTs to compare models and their more parameter rich extensions.
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