[Spatial structure optimization simulation of main forest types in Great Xing'an Mountains, Northeast China]. / å¤§å ´å®‰å²­ä¸»è¦æ£®æž—ç±»åž‹æž—åˆ†ç©ºé—´ç»“æž„ä¼˜åŒ–æ¨¡æ‹Ÿ.

Based on the data from four 1 hm2 permanent plots in main forest types [namely natural Larix gmelinii forest (LF), natural Betula platyphylla forest (BF), coniferous-broadleaved mixed forest (CBMF) and coniferous mixed forest (CMF)] in Great Xing'an Mountains, a comprehensive cutting index of individual tree (T), based on the commonly used spatial structure parameters [i.e., mingling (M), neighborhood comparison (U), uniform angle index (W), and competition index (CI)] and non-spatial structure parameters [tree vigor index (DC), tree stability index (DH)], was constructed using combined AHP and entropy evaluation method. The cutting process was simulated by Excel VBA to determine the best tending intensity on the basis of systematic comparison of comprehensive T-value under different tending intensities (10%, 20%, and 30%) of different forest types. The results showed that, in the initial state, the mean values of W were all 0.57, indicating a typical cluster distribution. The mean values of U ranged from 0.50 to 0.51 and the dominant degree of overall growth of trees was in a typical mean state. The mixed degree of four main forest types was generally low, with the mixed forest being obviously higher than the pure forest. The mean competition index within the stand was above 2.0, indicating higher competition pressure. The stability and growth vigor index of LF were significantly higher than those of other stands. Overall, the management urgency of BF was significantly higher than that of other stands. With regard to T-value growth rate between adjacent tending intensities, the optimal cutting intensity was 30% for LF forest and 10% for other types. The relative growth rates were 9.7%, 7.9%, 6.6% and 3.9% respectively. However, from the perspective of T-value and canopy density with different tending intensities, the optimal cutting intensity of BF was 20%, and the others were all 30%, in which the T-values were increased by 28.9%, 16.4%, 17.5% and 9.2% respectively. After simulated harvesting, stand structure was improved in various degrees and the mixed degree of tree species was increased. The horizontal distribution pattern of stand tended to random distribution. The dominance degree of dominant tree species was increased. The competition pressure of trees was decreased. DC of trees was slightly lower and the DH of trees was improved.

Prediction models and the extrapolation effects for water content of surface dead fuels in the typical stand of the Great Xing'an Mountains of China by one-hour time step.

The water content of surface dead fuels is one of the most important indicators for forecasting fire danger and fire behaviors. We employed the timelag equilibrium water content methods (i.e. Nelson and Simard models) and the meteorological variable regression method to continuously measure the water content of surface dead fuels by one-hour time step from September to October in 2010 under Populus davidiana + Betula platyphylla, Picea koraiensis and the cutover lands (Pinus sylvestris var. mongolica + Betula platyphylla) with different canopy densities in Pangu Forestry Bureau, the Great Xing'an Mountains, Heilongjiang Province, China. We established prediction models and obtained prediction errors. The models were also used to extrapolate the water contents of surface dead fuels under other forest stands and the extrapolation accuracy was analyzed. The results showed that the mean absolute error, the mean relative error and the mean square error root of Nelson model (0.0154, 0.104 and 0.0226) were lower than those of Simard model (0.0185, 0.117 and 0.0256). In terms of extrapolation effects, the mean absolute error, the mean relative error and the mean square error root of meteorological variable regression method (0.0410, 0.0300 and 0.0740) were lower than those of Simard model (0.610, 0.492 and 0.846), but they were higher than those of Nelson model (0.034, 0.021 and 0.0660). Such results indicated that the timelag equilibrium moisture content method by one-hour time step, especially Nelson model, was sui-table for the forest stands in the Great Xing'an Mountains. Although extrapolation could not reduce the prediction errors, it could help improve the prediction accuracy and the efficiency of the present models applied to different forest stands or in a larger scale. The modeling and extrapolation errors were closely related to species identity and canopy densities, thus the appropriate timelag equilibrium moisture content methods should be selected according to different forest stands and locations.

[Effects of fire severity and recovery time on organic carbon content of forest soil in Great Xing'an Mountains, China.] / ç«çƒ§å¼ºåº¦å’Œç«åŽæ¢å¤æ—¶é—´å¯¹å¤§å ´å®‰å²­æ£®æž—åœŸå£¤æœ‰æœºç¢³å«é‡çš„å½±å“.

Fire is one of the major factors that alter structure and function of forest in the Great Xing'an Mountains, with consequences on soil carbon cycling in forests. In this study, we collected soil samples (layers O, A, AB, BC, and C) under different fire-severity levels (low, moderate, and high) and post-fire recovery times (1987-2012) in the forests of Great Xing'an Mountains. Analysis of variance and multiple comparison were used to analyze effects of fire severity and reco-very time on content of soil organic carbon. The results showed that soil organic carbon (SOC) content in layer O presented a rising trend under both moderate- and high-severity fire disturbances. The content of SOC in layers A and B decreased year by year under low- and moderate-severity fires, which ranked in the order: 3 years > 5 years > 10 years > over 10 years since fire. The content of SOC under high-severity fire presented an increasing trend within 10 years since fire distur-bance and then decreased rapidly over 10 years. The content of SOC in layer BC presented no obvious changes.

[Simulating the effects of climate change and fire disturbance on aboveground biomass of boreal forests in the Great Xing'an Mountains, Northeast China]. / æ°”å€™å˜åŒ–å’Œæž—ç«å¹²æ‰°å¯¹å¤§å ´å®‰å²­æž—åŒºåœ°ä¸Šç”Ÿç‰©é‡å½±å“çš„åŠ¨æ€æ¨¡æ‹Ÿ.

Predicting the effects of climate warming and fire disturbance on forest aboveground biomass is a central task of studies in terrestrial ecosystem carbon cycle. The alteration of temperature, precipitation, and disturbance regimes induced by climate warming will affect the carbon dynamics of forest ecosystem. Boreal forest is an important forest type in China, the responses of which to climate warming and fire disturbance are increasingly obvious. In this study, we used a forest landscape model LANDIS PRO to simulate the effects of climate change on aboveground biomass of boreal forests in the Great Xing'an Mountains, and compared direct effects of climate warming and the effects of climate warming-induced fires on forest aboveground biomass. The results showed that the aboveground biomass in this area increased under climate warming scenarios and fire disturbance scenarios with increased intensity. Under the current climate and fire regime scenario, the aboveground biomass in this area was (97.14±5.78) t·hm-2, and the value would increase up to (97.93±5.83) t·hm-2 under the B1F2 scenario. Under the A2F3 scenario, aboveground biomass at landscape scale was relatively higher at the simulated periods of year 100-150 and year 150-200, and the value were (100.02±3.76) t·hm-2 and (110.56±4.08) t·hm-2, respectively. Compared to the current fire regime scenario, the predicted biomass at landscape scale was increased by (0.56±1.45) t·hm-2 under the CF2 scenario (fire intensity increased by 30%) at some simulated periods, and the aboveground biomass was reduced by (7.39±1.79) t·hm-2 in CF3 scenario (fire intensity increased by 230%) at the entire simulation period. There were significantly different responses between coniferous and broadleaved species under future climate warming scenarios, in that the simulated biomass for both Larix gmelinii and Betula platyphylla showed decreasing trend with climate change, whereas the simulated biomass for Pinus sylvestris var. mongolica, Picea koraiensis and Populus davidiana showed increasing trend at different degrees during the entire simulation period. There was a time lag for the direct effect of climate warming on biomass for coniferous and broadleaved species. The response time of coniferous species to climate warming was 25-30 years, which was longer than that for broadleaf species. The forest landscape in the Great Xing'an Mountains was sensitive to the interactive effect of climate warming (high CO2 emissions) and high intensity fire disturbance. Future climate warming and high intensity forest fire disturbance would significantly change the composition and structure of forest ecosystem.

[Drivers of human-caused fire occurrence and its variation trend under climate change in the Great Xing'an Mountains, Northeast China]. / å¤§å ´å®‰å²­äººä¸ºç«å‘ç”Ÿå½±å“å› ç´ åŠæ°”å€™å˜åŒ–ä¸‹çš„è¶‹åŠ¿.

The Great Xing'an Mountains are an important boreal forest region in China with high frequency of fire occurrences. With climate change, this region may have a substantial change in fire frequency. Building the relationship between spatial pattern of human-caused fire occurrence and its influencing factors, and predicting the spatial patterns of human-caused fires under climate change scenarios are important for fire management and carbon balance in boreal forests. We employed a spatial point pattern model to explore the relationship between the spatial pattern of human-caused fire occurrence and its influencing factors based on a database of historical fire records (1967-2006) in the Great Xing'an Mountains. The fire occurrence time was used as dependent variable. Nine abiotic (annual temperature and precipitation, elevation, aspect, and slope), biotic (vegetation type), and human factors (distance to the nearest road, road density, and distance to the nearest settlement) were selected as explanatory variables. We substituted the climate scenario data (RCP 2.6 and RCP 8.5) for the current climate data to predict the future spatial patterns of human-caused fire occurrence in 2050. Our results showed that the point pattern progress (PPP) model was an effective tool to predict the future relationship between fire occurrence and its spatial covariates. The climatic variables might significantly affect human-caused fire occurrence, while vegetation type, elevation and human variables were important predictors of human-caused fire occurrence. The human-caused fire occurrence probability was expected to increase in the south of the area, and the north and the area along the main roads would also become areas with high human-caused fire occurrence. The human-caused fire occurrence would increase by 72.2% under the RCP 2.6 scenario and by 166.7% under the RCP 8.5 scenario in 2050. Under climate change scenarios, the spatial patterns of human-caused fires were mainly influenced by the climate and human factors.

[C:N:P stoichiometry characteristics of litter and soil of forests in Great Xing'an Mountains with different fire years.] / å¤§å ´å®‰å²­ä¸åŒç«çƒ§å¹´é™æ£®æž—å‡‹è½ç‰©å’ŒåœŸå£¤C、N、P化学计量特征.

We investigated the fire impacts on nutrients in litter and soil, and their C:N:P stoichio-metry in forests of Great Xing'an Mountains. The studied sites differed in their burning year (post-fire 4, 14, 40, 70 years and unburned within 120 years) and had different topographic locations (sloped land and flat land). The results showed that there were significant differences in stoichio-metry characteristics of C, N, P for both litter and soil with different burning years. No significant fluctuation was observed for the litter C content, while the contents of litter N and P increased with the increasing post-fire recovery years. In specific, we found the contents of litter N and P decreased at post-fire 4 and 14 years and nearly recovered to the control level at 40 years after fire. Additionally, C:N and C:P ratios of litter decreased, but N:P ratio of litter increased following post-fire recovery time. The contents of C, N, P and their ratios (C:N, C:P and N:P) in soil decreased with soil depth. Soil C content exhibited an increasing trend following post-fire recovery time and was significantly higher than the control at post-fire 70 years in sloped land, but no significant difference in the flat land. Significant interactive effects between fire history and slope were observed in soil P content and C:P ratio. Soil P content was higher than the control at post-fire 4 years in sloped land, but was higher than the control at post-fire 40 years in flat land. The C:P ratio recovered to the control level at post-fire 14 years in sloped land, and there was no significant diffe-rence in flat land. Redundancy analysis showed that slope effect played a more vital role than fire history effect in soil organic layer, while fire history effect was the most important factor for the varia-tion of soil nutrients in soil mineral layer. In our study, nutrients of litter and soil were lower than the control level at post-fire 4 and 14 years. The quality of litter and soil was improved with accele-rated plant growth and litter decomposition following post-fire recovery time and recovered to the pre-fire level at post-fire 40 years, reaching a steady status.

[Influences of meteorological factors on larch caterpillar population.] / æ°”è±¡å› å­å¯¹è½å¶æ¾æ¯›è™«ç§ç¾¤æ•°é‡çš„å½±å“.

To explore the relationship between larch caterpillar population and meteorological factors, a suite of linear regression models were developed. We used a stepwise regression approach to obtain the best model based on the Akaike information criterion (AIC). We also identified the key meteorological factors based on relative weight, and analyzed their marginal influences on larch ca-terpillar population. Our modeling results showed that meteorological conditions during the young larva stage and breeding stage played a key role in impacting larch caterpillar population. In contrast, meteorological conditions during the middle larva stage and old larva stage had a weaker effect. The mean daily relative humidity during young larva stage, the accumulated daily temperature less than -22 ℃ during young larva's overwintering stage, and the total rainfall in breeding stage were the key meteorological factors affecting the population of larch caterpillar. With the increase of one standard deviation from the mean daily relative humidity during young larva stage and the total rainfall in breeding stage, the larch caterpillar population would be reduced by 62% and 35% of standard deviation, respectively. In contrast, one standard deviation increase of the accumulated daily temperature less than -22 ℃ during young larva's overwintering stage would increase larch caterpillar population by 40% of standard deviation. Our study suggests that the larch caterpillar population in the future may explode in response to global warming, and its infestation could exhibit a new pattern. It is therefore very important to establish a long-term population monitoring system.

[Dynamics and prediction models of ground surface dead fuel moisture content for typical stands in Great Xing'an Mountains, Northeast China.] / å¤§å ´å®‰å²­å ¸åž‹æž—åˆ†åœ°è¡¨æ­»å¯ç‡ƒç‰©å«æ°´çŽ‡åŠ¨æ€å˜åŒ–åŠé¢„æµ‹æ¨¡åž‹.

The fuel moisture content dynamics of mixed forest of Populus davidiana-Betula platyphylla, Larix gmelinii, Pinus sylvestris var. mongolica, mixed forest of L. gmelinii-B. platyphylla, B. platyphylla at different slope positions in spring and autumn were investigated in Xilinji Forestry Bureau ofthe Great Xing'an Mountains region. The moisture content prediction models of different stands in different seasons were established and the predicted errors were analyzed.The results showed that the fuel moisture content in the same stand varied with slope position. The mean absolute error of Nelson model (0.13) was lower than that of Simard model (0.14), and was significantly lower than that of meteorological element regression model (0.25). The prediction accuracy of the autumn model was higher than the spring model and spring-autumn mixed model.