Reconstructed vegetation cover, related factors and prescribed values in previous studies.

<p><i>Vegetation Cover</i> shows the reconstructed NDVI averaged from 15 sequences, with linear regression after 6.5 ka yr BP showing a decrease of 0.13. The light green area indicates the upper quartile and lower quartile of the 15 sequences, indicating that areas with higher NDVI experience larger fluctuations and were the major contributors to the vegetation decline in <i>Period II</i>. Sand% of AN shows the coarse sand (>63 μm) percentage in sediment cores from Anguli Nuur (inversely scaled). Stalagmite δ¹⁸O of Dongge Cave (as δ¹⁸O, values are negative), coarse sand percentage of Anguli Nuur and reconstructed NDVI are significantly correlated with each other. Prescribed values in the model simulation by Dallmeyer and Claussen (2011) coincide with the vegetation cover at 6.5 cal ka BP as reconstructed here; the colour bar shows the percentage of grass/trees in comparison to the modern vegetation distribution.</p

Quantifying Regional Vegetation Cover Variability in North China during the Holocene: Implications for Climate Feedback

<div><p>Validating model simulations of vegetation-climate feedback needs information not only on changes in past vegetation types as reconstructed by palynologists, but also on other proxies such as vegetation cover. We present here a quantitative regional vegetation cover reconstruction for North China during the Holocene. The reconstruction was based on 15 high-quality lake sediment profiles selected from 55 published sites in North China, along with their modern remote sensing vegetation index. We used the surface soil pollen percentage to build three pollen-vegetation cover transfer models, and used lake surface sediment pollen data to validate their accuracy. Our results showed that vegetation cover in North China increased slightly before its maximum at 6.5 cal ka BP and has since declined significantly. The vegetation decline since 6.5 cal ka BP has likely induced a regional albedo change and aerosol increase. Further comparison with paleoclimate and paleovegetation dynamics in South China reproduced the regional cooling effect of vegetation cover decline in North China modelled in previous work. Our discussion demonstrates that, instead of reconstructing vegetation type from a single site, reconstructing quantitative regional vegetation cover could offer a broader understanding of regional vegetation-climate feedback.</p></div

Mean reconstructed vegetation cover of each profile for 1–2, 6–7 and 9–10 ka BP.

<p>The circle diameters show the mean values of the reconstruction within 1 ka, while modern NDVI and modern MAP are plotted in the background. Because the heterogeneity between sites is much larger than the variation, the diameters of the circles were plotted according to the relative values compared to the mean value of the entire time series at each site.</p

Result of model reconstruction and verification.

<p>In subplot A, LR, MAT and ANN all performed well in the model construction phase and passed the 0.01 significance level test; in subplot B, when verified by lake surface sediment pollen data, MAT failed to produce a reliable result. ANN was chosen for the reconstruction. All R² data are the adjusted R square.</p

Mechanisms discussed in this study.

<p>Black boxes show the mechanisms discussed in the text, and are further explained by the blue annotations or the corresponding R² in the linear correlation. While vegetation cover in North China is mainly controlled by precipitation, its decline in the past 6500 years might have led to the changes in both land cover albedo and aerosol production, with a resulting regional cooling effect, as model simulations in previous studies and the regional comparison in this study have shown.</p

Sample locations and modern NDVI distribution.

<p>Modern NDVI data were acquired by averaging data for August from 1982–2006 of the GIMMS dataset. NDVI in South China is homogeneous at a high level, while that of North China varies widely with precipitation. Samples of NDVI-MAP relationships were randomly chosen from grid points with natural vegetation in our study area (North China) and fitted by a logistic curve. Sites with surface soil pollen, lake surface pollen and sediment profiles are distributed around the 400 mm isohyet; some sites with surface soil pollen samples are located in Mongolia, but in the same biome and precipitation regime. <i>T of China N.</i> and <i>T of China S.</i> indicate the paleotemperature records used in the temperature reconstruction of North China and South China <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071681#pone.0071681-Fang1" target="_blank">[70]</a>, respectively.</p

Experimental paradigm. Following a 500 ms warning signal (+), the Stroop stimuli was presented.

<p>The distractor word (positive, negative or depression-related) was presented for 200 ms, and the target face (positive or negative) was present for no more than 1000 ms. Subjects were asked to identify the valence of faces while ignoring words overlaid on them within 2000 ms. The target face and the following blank will disappear after the response. The inter-stimulus interval (ISI) was 1000 ms. Emotional incongruent (EI) trials consisted of faces and words with opposing valence, and emotional congruent (EC) trials consisted of faces and words with identical valence. Faces were presented in color and words in blue.</p

Mean reaction times (ms) and error rates (% in brackets) for traditional emotional conflict effect in MDD patients and healthy controls.

<p> <i>Note.</i></p><p>**<i>p</i><.01. Abbreviations: NN represents a combination of negative word distractor and negative face target; PN, positive distractor – negative target; PP, positive distractor – positive target; NP, negative distractor – positive target.</p

Demographic and clinical data for MDD patients and healthy controls.

<p>Demographic and clinical data for MDD patients and healthy controls.</p

Mean reaction times (ms) and error rates (% in brackets) for depression-related emotional conflict effect in MDD patients and healthy controls.

<p> <i>Note.</i></p><p>*<i>p</i><.05,</p><p>**<i>p</i><.01. Abbreviations: DN represents a combination of depression-related word distractor and negative face target; PN, positive distractor – negative target; PP, positive distractor – positive target; DP, depression-related distractor – positive target.</p