Same as in Fig 4, but for the SATAs from the 160-station monthly dataset for China.

<p>Same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171641#pone.0171641.g004" target="_blank">Fig 4</a>, but for the SATAs from the 160-station monthly dataset for China.</p

Same as in Fig 3, but for the SATAs.

<p>Same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171641#pone.0171641.g003" target="_blank">Fig 3</a>, but for the SATAs.</p

Geographical distribution of the WTWR of sea level pressure anomalies (SLPAs) in the Eurasian continent for the starting month of February.

<p>Geographical distribution of the WTWR of sea level pressure anomalies (SLPAs) in the Eurasian continent for the starting month of February.</p

Lag correlations between the SLPAs in February and the SLPAs in each subsequent month over East Asia (45°N–75°N, 100°E–140°E).

<p>The thin solid line represents the 90% confidence level.</p

The geographical distributions of the index <i>χ</i>.

<p>The geographical distributions of the index <i>χ</i>.</p

Spatial distributions of the standard deviation in the global SST anomaly time series during the time 1870–2009.

<p>Spatial distributions of the standard deviation in the global SST anomaly time series during the time 1870–2009.</p

Spatial distributions of the scaling exponents in the global SST anomaly time series during the time 1870–2009 employing DFA2.

<p>Spatial distributions of the scaling exponents in the global SST anomaly time series during the time 1870–2009 employing DFA2.</p

MOESM1 of Exploring the spatiotemporal drivers of malaria elimination in Europe

Additional file 1: Details on reported years for malaria elimination for European countries

Log-log plots of power-law relationship between the detrended variability <i>F</i>(<i>s</i>) and the time scale <i>s</i> in the global zones, southern hemisphere, north hemisphere, the middle latitude zones, and the tropics (solid squares for the SST series (annual cycles are removed) using DFA2 and red solid lines for the records represent linear fit of SST fluctuations).

<p>Log-log plots of power-law relationship between the detrended variability <i>F</i>(<i>s</i>) and the time scale <i>s</i> in the global zones, southern hemisphere, north hemisphere, the middle latitude zones, and the tropics (solid squares for the SST series (annual cycles are removed) using DFA2 and red solid lines for the records represent linear fit of SST fluctuations).</p

MOESM2 of Exploring the spatiotemporal drivers of malaria elimination in Europe

Additional file 2: Results of statistical tests examining the differences in climatic, land use and demographic variables between European countries and those focused on eliminating the disease at the time of writing