Response of spatial vegetation distribution in China to climate changes since the Last Glacial Maximum (LGM)

<div><p>Analyzing how climate change affects vegetation distribution is one of the central issues of global change ecology as this has important implications for the carbon budget of terrestrial vegetation. Mapping vegetation distribution under historical climate scenarios is essential for understanding the response of vegetation distribution to future climatic changes. The reconstructions of palaeovegetation based on pollen data provide a useful method to understand the relationship between climate and vegetation distribution. However, this method is limited in time and space. Here, using species distribution model (SDM) approaches, we explored the climatic determinants of contemporary vegetation distribution and reconstructed the distribution of Chinese vegetation during the Last Glacial Maximum (LGM, 18,000 ¹⁴C yr BP) and Middle-Holocene (MH, 6000 ¹⁴C yr BP). The dynamics of vegetation distribution since the LGM reconstructed by SDMs were largely consistent with those based on pollen data, suggesting that the SDM approach is a useful tool for studying historical vegetation dynamics and its response to climate change across time and space. Comparison between the modeled contemporary potential natural vegetation distribution and the observed contemporary distribution suggests that temperate deciduous forests, subtropical evergreen broadleaf forests, temperate deciduous shrublands and temperate steppe have low range fillings and are strongly influenced by human activities. In general, the Tibetan Plateau, North and Northeast China, and the areas near the 30°N in Central and Southeast China appeared to have experienced the highest turnover in vegetation due to climate change from the LGM to the present.</p></div

The AUC values of the Generalized Linear Models (GLMs) and MaxEnt for each vegetation type.

<p>The AUC values of the Generalized Linear Models (GLMs) and MaxEnt for each vegetation type.</p

Percentage of persistence and changing vegetation distribution (A) from the Last Glacial Maximum (LGM) to the Mid-Holocene (MH), (B) from the MH to the present, and (C) from the LGM to the present.

<p>Dark blue: percentage of a vegetation type that remained unchanged between two time periods. Orange: percentage of a vegetation type that was converted to the most abundant new vegetation type (indicated with the type number in the orange bar). Grey: percentage of a vegetation type that was converted to all other vegetation types. For example, 40% of the LGM distribution range of veg 1 (taiga forest) remained unchanged at the present, 49% was converted to veg 17 (grass and forb meadow) in the MH and 11% were converted to other vegetation types (C).</p

Comparison between model-based and pollen-based vegetation reconstructions during the Last Glacial Maximum (LGM).

<p>Panels (A) to (E) map the hindcasted climate suitability of the shown vegetation types, while panels (F) to (J) map the hindcasted binary distribution of each vegetation type. The blue dots in the maps represent the pollen sites for which the same vegetation type was identified during the LGM.</p

Hindcasted past distribution of natural vegetation during (A) the Last Glacial Maximum (LGM) and (B) the Mid-Holocene (MH) and (C) the proportion (%) of each vegetation type (LGM: dark blue line; MH: orange line; modeled: dark gray line).

<p>In (C), the proportion (%) of a vegetation type was calculated as the ratio of its distribution range relative to the terrestrial area of China.</p

Comparison between model-based and pollen-based vegetation reconstructions during the Mid-Holocene (MH).

<p>Panels (A) to (G) map the hindcasted climatic suitability of the shown vegetation types, while panels (H) to (N) map the hindcasted binary distribution of each vegetation type. The blue dots in the maps represent the pollen sites for which the same vegetation type was identified for the MH.</p

Kappa values between model-based and pollen-based vegetation reconstructions in the LGM and the MH for seven major vegetation types in both LGM and MH.

<p>Kappa values between model-based and pollen-based vegetation reconstructions in the LGM and the MH for seven major vegetation types in both LGM and MH.</p

Contemporary vegetation distribution in China.

<p>(A) Observed (white area are croplands, urban areas and planted forests); (B) modeled; (C) the proportions of each vegetation type to the terrestrial area of China (observed: light gray line; modeled: dark gray line) and their range fillings calculated as the ratios between the observed and modeled distribution ranges (red line). Due to strong human disturbance, veg 5, 6, and 8 have experienced significant deforestation at the present, and therefore the proportions of observed vegetation types do not sum up to 100%.</p

Quantitative Assessment of the Effect of <i>KCNJ11</i> Gene Polymorphism on the Risk of Type 2 Diabetes

<div><p>To clarify the role of potassium inwardly-rectifying-channel, subfamily-J, member 11 (<i>KCNJ11</i>) variation in susceptibility to type 2 diabetes (T2D), we performed a systematic meta-analysis to investigate the association between the <i>KCNJ11</i> E23K polymorphism (rs5219) and the T2D in different genetic models. Databases including PubMed, Medline, EMBASE, and ISI Web of Science were searched to identify relevant studies. A total of 48 published studies involving 56,349 T2D cases, 81,800 controls, and 483 family trios were included in this meta-analysis. Overall, the E23K polymorphism was significantly associated with increased T2D risk with per-allele odds ratio (OR) of 1.12 (95% CI: 1.09–1.16; <i>P</i><10⁻⁵). The summary OR for T2D was 1.09 (95% CI: 1.03–1.14; <i>P</i><10⁻⁵), and 1.26 (95% CI: 1.17–1.35; <i>P</i><10⁻⁵), for heterozygous and homozygous, respectively. Similar results were also detected under dominant and recessive genetic models. When stratified by ethnicity, significantly increased risks were found for the polymorphism in Caucasians and East Asians. However, no such associations were detected among Indian and other ethnic populations. Significant associations were also observed in the stratified analyses according to different mean BMI of cases and sample size. Although significant between study heterogeneity was identified, meta-regression analysis suggested that the BMI of controls significantly correlated with the magnitude of the genetic effect. The current meta-analysis demonstrated that a modest but statistically significant effect of the 23K allele of rs5219 polymorphism in susceptibility to T2D. But the contribution of its genetic variants to the epidemic of T2D in Indian and other ethnic populations appears to be relatively low.</p></div

Results of meta-analysis for <i>KCNJ11</i> E23K polymorphism and T2D risk.

<p><i>P</i>(Z): Z test used to determine the significance of the overall OR.</p><p><i>P</i>(Q)^a: Cochran's chi-square Q statistic test used to assess the heterogeneity in subgroups.</p><p><i>P</i>(Q)^b: Cochran's chi-square Q statistic test used to assess the heterogeneity between subgroups.</p