Data for PONE-D-15-15932

Didymodon ( including species data, sampling plots, and habitat characteristics collected in the field work ) and environmental variables (bioclimatic,topographic,and vegetation data) which were projected as GIS raster layers in GCS_WGS_1984 coordinate system, and converted to ASCII format for modeling the distribution of Didymodon in Tibet

Number of <i>Didymodon</i> species along the altitude gradient and under different precipitation regimes in Tibet.

<p>Number of <i>Didymodon</i> species along the altitude gradient and under different precipitation regimes in Tibet.</p

The presence probability of <i>Didymodon</i> spatial distributions in Tibet.

<p>The red circles represent the <i>Didymodon</i> species in the plots that were investigated.</p

Correlation of species diversity and environmental factors affecting <i>Didymodon</i> in the study area.

<p><b>Note</b>:</p><p>* and ** represent statistically significant correlations.</p><p>*: p < 0.05</p><p>**: p < 0.01</p><p>Veg-type represents vegetation type, Veg-cove represents vegetation cover, Temp represents temperature, Soil-mois represents soil moisture at a depth of 3.8 cm, and Soil-mois2 represents soil moisture at a depth of 7.6 cm.</p><p>Correlation of species diversity and environmental factors affecting <i>Didymodon</i> in the study area.</p

<i>Didymodon</i> species identified in Tibet, and their relative frequency, coverage, and importance value.

<p><i>Didymodon</i> species identified in Tibet, and their relative frequency, coverage, and importance value.</p

The importance of 22 environmental variables in modeling the distribution of <i>Didymodon</i> in Tibet.

<p>The training gain describes how much better the MaxEnt distribution fits the presence data compared to a uniform distribution. The names and descriptions of environmental variables are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132346#pone.0132346.t002" target="_blank">Table 2</a>. The white squares represent the effect of removing a single variable from the full model. The gray squares represent the training gains when using only one environmental variable in MaxEnt. The black square represents the training gains when all variables were run in MaxEnt (1.61).</p