Mixed effects of landscape complexity and stand factors on cytospora canker of poplar across two different field soil conditions
-
摘要:目的本文探究了不同土壤条件下景观复杂性和林分因素对杨树腐烂病的影响机制，为合理配置景观格局从而有效控制杨树腐烂病提供理论依据，促进杨树人工林的可持续发展。方法于2018年和2019年分别调查了新疆维吾尔自治区克拉玛依市和玛纳斯县两种不同的土壤理化性质下杨树腐烂病的发病情况。通过建立广义线性混合效应模型（GLMMs），以年份为随机效应，在景观尺度上分析景观复杂性和林分因素对杨树腐烂病的发生影响。结果黑杨和新疆杨腐烂病的发生与林分密度和害虫的香农多样性指数呈显著正相关关系，黑杨腐烂病与第一活枝高有显著正相关关系。两种杨树的腐烂病的发生与非杨树寄主的比例，以及海拔高度呈负相关关系；腐烂病与土壤理化性质具有显著相关性，同一景观结构中，克拉玛依的病害发病率高于玛纳斯，而且同一土壤条件下，简单景观中腐烂病的发病率高于复杂景观。结论本研究表明可以通过样地管理和在景观尺度上增强树木抗性来降低杨树腐烂病的发病率，从而为森林有害生物的管理提供理论依据。在造林规划中要考虑周边的景观结构特征对有害生物的潜在影响。同时利用土地覆被数据长期有效地监管景观特征对杨树腐烂病的影响，可以达到改善经济和环境效益的目的。Abstract:ObjectiveThis paper explores the influence mechanism of landscape complexity and stand factors on cytospora canker of poplar caused by Cytospora chrysosperma across two different field soil conditions, to provide theoretical basis for rational allocation of landscape patterns to control cytospora canker effectively, and to further develop sustainable poplar (Populus spp.) plantations.MethodWe spent 2 years (2018−2019) collecting cytospora canker data across two different soil physical and chemical properties in Xinjiang Uygur Autonomous Region of wester China: one in Karamay City and another in Manasi County. Using general linear mixed effect models (GLMMs) with the random effect of year to analyze how the landscape complexity and stand factors influence cytospora canker at landscape scale.ResultResults showed that the cytospora canker of Populus nigra and P. alba var. pyramidalis both had significantly positive relationships with tree density, as well as the Shannon diversity index of pests. Compared with P. alba var. pyramidalis, P. nigra had a significant relationship with the height of first live branch. The cytospora canker of the two tree species was both correlated negatively with the proportion of non-poplar hosts, as well as elevation. The cytospora canker had a significant correlation with physical and chemical properties of soil, in the same landscape structure, the disease incidence in Karamay City was higher than that in Manasi County. Under the same soil conditions, the incidence of cytospora canker in simple landscape was higher than that in complex landscape.ConclusionOverall, our study shows that the occurrence of cytospora canker on poplar can be reduced via management of habitats and enhanced tree resistance at landscape scale, and provides valuable contributions to landscape research and for managing outbreaks of insects and pathogens. In afforestation planning, the potential impact of the surrounding landscape structure on insects and pathogens should be considered. The effects of landscape features on cytospora canker of poplar can be monitored on a large scale and over long periods of time using land cover data to improve economic and environmental benefits.
-
Keywords:
- cytospora canker /
- poplar /
- plantation /
- landscape complexity /
- soil
-
有数据显示，全球陆地土壤碳含量为1 750 Pg，植被碳含量为550 Pg，总量约为大气碳含量的3倍[1]，即无论土壤作为“碳源”还是“碳汇”都将对气候变化产生巨大影响，故在全球气候变化和生态扰动背景下，陆地生态系统有机碳（SOC）分布与动态变化受到广大学者的广泛重视。尤其表层土壤，是SOC的集中分布层，其SOC含量更显著的受到微地形[2]、植被类型[3-4]、枯落物数量[5-6]、人为扰动[7]等因素的影响，使其在空间分布上具有更强的异质性，而成为研究者关注的一个焦点。
在不同空间尺度上，SOC分布的异质性为SOC储量的科学估算及关键影响因素的研究提出了挑战，故随着地统计分析工具的出现，广大学者采用地统计学的方法针对不同生态类型、空间尺度等条件下SOC的分布特征及空间变异性进行了研究，并表明地统计学是研究SOC空间分布的一种有效工具[8]。如Gao等[9]基于地统计方法研究得出中国北方丘陵地区SOC的空间异质性受随机性和结构性因素的共同控制。Göl等[10]研究发现球状模型可以很好地模拟黑海不同土地利用方式SOC的空间分布。SOC的空间分布还具有尺度效应[11]，然而在区域、县域等大、中尺度下的空间分布与预测往往简化了某些生态过程，可能增加碳研究的不确定性[12]，相比之下，小尺度下的空间预测反映的信息量更加丰富[13]，因此揭示小尺度SOC的空间分布格局及其影响因素对于精确估算SOC含量是必要的。
青藏高原属于典型的生态脆弱区和气候变化的敏感区，在全国乃至全球都具有重要的生态地位。近年来，随着交通不断便利，不少学者开展了青藏高原土壤碳循环研究，如王建林等[14]关于草地土壤化学计量特征的大尺度空间分布研究，从宏观上了揭示青藏高原的土壤养分状况。叶成龙等[15]人基于野外定点试验和室内试验的研究表明，氮输入将增加预测青藏高原高寒草甸地区土壤碳排放的不确定性。其他研究如吕韶利[16]、刘合满[17]等，研究结果为高寒地区土壤碳循环研究奠定了丰富的理论和技术参考。青藏高原地形、植被、气候等具有显著的差异，导致SOC含量具有高度的空间异质性，然而目前针对SOC的空间分布与模拟研究仍少见报道，尤其是在小尺度空间范围，这将阻碍我们对于青藏高原这一特殊生态系统土壤碳动态变化的认识。为科学阐明高寒地区典型植被类型表层SOC及SOCD的空间分布特征与影响因素，本研究以西藏东南部色季拉山西坡广泛分布的草甸和杜鹃灌丛土壤为研究对象，采用地统计分析方法，旨在阐明小尺度下不同植被类型SOC及SOCD的空间变异及格局，以期为高寒环境下土壤有机碳循环研究及土壤碳储量的准确估算提供理论支撑。
1. 研究区概况与研究方法
1.1 研究区概况
本研究以色季拉山西坡相邻分布的灌丛、草甸为研究样地，其海拔4 200 ~ 4 400 m，地理位置29°36′N、94°38′E。根据色季拉山道班阴坡林外（29°35′51″N，94°36′43″E，4 390 m）2005 — 2012年的观测数据，该区域年均气温约为0.64 ℃，年均相对湿度为77%，年降雨量为877 mm。土壤类型属于亚高山林灌草甸土、沼泽草甸土[18]。
本研究所选样区主要包含苔草高寒草甸（Carex alpine meadow，CAM）、林芝杜鹃灌丛（Rhododendron tanastylum shrub，RTS）、雪山杜鹃灌丛（Rhododendron aganniphum shrub，RAS）3种植被类型。苔草高寒草甸，地面以苔草为建群种，其中还分布有委陵菜（Potentilla chinensis）、老鹳草（Geranium wilfordii）、阜莱氏马先蒿（Pedicularis fletcherii）、西藏糙苏（Phlomis tibetica）、偃卧繁缕（Stellaria decumbens）、蒲公英（Taraxacu mmongolicum）等，地面凹凸不平，存在沟壑。林芝杜鹃灌丛是以林芝杜鹃（Rhododendron tanastylum）为优势种，红枝小檗（Berberis erythroclada）和雪层杜鹃（Rhododendron nivale）为亚优势种的灌草丛地，植被分布不均一。雪山杜鹃灌丛主要生长雪山杜鹃（Rhododendron aganniphum），并伴有高山柏（Sabina squamata）和急尖长苞冷杉（Abies georgei var. smithii）零星分布，地面有较厚的枯落物层（最厚可达10 cm）。样地概况如表1所示。
表 1 样地基本概况Table 1. Basic condition of sample plots植被类型
Vegetation
type海拔
Altitude/m植被盖度
Vegetation
coverage/%坡度
Slope/(°)全氮
Total nitrogen
(TN)/(g·kg−1)全磷
Total phosphorus
(TP)/(g·kg−1)pH 含水率
Water
content/%土壤密度
Soil density/
(g·cm−3)黏粒
Clay/%粉粒
Silt/%砂粒
Sand/%CAM 4242 95 16 6.54 1.12 5.59 55.37 0.81 3.82 62.76 33.42 RTS 4264 75 14 3.29 0.39 5.47 72.01 0.82 3.55 61.45 35.00 RAS 4387 > 95 25 5.48 0.37 4.30 72.62 0.46 3.19 62.07 34.75 注：CAM为苔草高寒草甸；RTS为林芝杜鹃灌丛；RAS为雪山杜鹃灌丛。黏粒粒径< 0.002 mm，粉粒粒径在0.002~0.05 mm之间，砂粒粒径在0.05~2 mm之间。下同。Notes: CAM is Carex alpine meadow; RTS is Rhododendron tanastylum shrub; RAS is Rhododendron aganniphum shrub. Clay particle size is less than 0.002 mm; silt particle size is between 0.002−0.05 mm; sand particle size is between 0.05−2 mm. The same below. 1.2 样品采集与处理
于2018年10月，在野外考察的基础上，选择坡度较缓，植被类型连续的区域作为研究区（图1），采用10 m × 10 m网格法采样。即在3种植被类型区采用测量绳分别划定一个50 m × 50 m的样地，然后按照10 m × 10 m网格设置采样点，共计36个采样点。分别采集0 ~ 10 cm层次土壤和原状土壤样品，共获得土壤样品和环刀土样各108个，详细记录每一采样点的地形（海拔、坡度、坡位、凸起、沟壑）、植被覆盖等信息。采集的样品去杂置于风干盘自然风干后，经研磨使其分别通过2.00、0.25 mm土壤筛，用以测定土壤碳、氮等理化性质。SOC采用重铬酸钾容量−外加热法测定，全氮（TN）采用半微量凯氏法测定，全磷（TP）采用钼锑抗比色法测定，土壤密度、机械组成分别采用环刀法和激光粒度分布仪（Bettersize2000，丹东百特仪器有限公司）测定，以上实验过程参照文献[19-20]进行。
1.3 土壤有机碳密度计算
SOCD的计算采用如下公式：
SOCD=CDE(1−G)/100 式中：
C 为有机碳含量（g/kg），D 为土壤密度（g/cm3），E 为土层厚度（cm），G 为直径 > 2 mm石砾百分含量（%），这里忽略G 后计算SOCD[21]。1.4 数据处理与分析
在SPSS 22.0中计算SOC及SOCD的最大值、最小值、平均值、标准差、变异系数（Coefficient of variation, CV），其中CV值在0 ~ 10%、10% ~ 100%和 > 100%之间时，分别属于弱、中等和强变异。对不同植被类型SOC、SOCD的差异显著性检验采用LSD法（P < 0.01）。在GS+ 9.0中建立半方差函数模型，模型拟合可以得到块金值（C0）、基台值（C0 + C）、变程（A0）3个评价空间变异程度的参数，其中块金值与基台值的比值（C0/（C0 + C））即结构比，反映了随机性因素引起的空间变异占系统总变异的比值，比值在 < 25%、25% ~ 75%、> 75%范围，分别表示强、中、弱的空间自相关性，随机性因素引起的空间变异逐渐增大[22]，变程反映了区域化变量存在空间自相关性的距离上限。基于半方差函数模型，在ArcGIS10.2中完成克里格插值。冗余分析采用Canoco 5.0进行，并采用Forward selection和Monte Carlo permutation test进行环境因子重要性排序和显著性检验。
2. 结果与分析
2.1 SOC及SOCD的分布特征
不同植被类型的SOC、SOCD差异显著（P < 0.01）（表2），SOC含量表现为RAS（146.45 g/kg） > CAM（95.60 g/kg） > RTS（60.43 g/kg），RAS分别较CAM和RTS的SOC含量高53.19%、142.35%，SOCD表现为CAM（7.34 kg/m2） > RAS（6.32 kg/m2） > RTS（4.80 kg/m2），CAM分别较RAS和RTS高22.31%、61.04%。该研究区SOC和SOCD均具有中等程度的空间变异。
表 2 SOC及SOCD的描述性统计特征Table 2. Descriptive statistics of SOC and SOCD指标
Item植被类型
Vegetation type样本量
Sample size平均值
Mean最大值
Max.最小值
Min.变异系数
CV/%P 分布类型
Distribution typeSOC/（g·kg−1） CAM 33 95.60 ± 44.31b 219.85 49.63 46.35 0.169 N RTS 36 60.43 ± 25.73a 148.31 24.01 42.58 0.359 N RAS 36 146.45 ± 49.67c 252.15 69.10 33.92 0.500 N SOCD/（kg·m−2） CAM 36 7.73 ± 2.39c 12.77 4.50 30.92 0.538 L RTS 36 4.80 ± 2.01a 8.92 1.90 41.88 0.943 N RAS 36 6.32 ± 1.98b 11.22 1.53 31.33 0.747 N 注：不同小写字母表示植被类型之间SOC或SOCD差异显著（P < 0.01）。N为正态分布；L为对数正态分布。Notes: different lowercase letters indicate significant differences in SOC or SOCD between vegetation types （P < 0.01）. N means normal distribution; L means lognormal distribution. 在K-S检验结果如表2所示，CAM的SOC在剔除3个异常值后符合正态分布，SOCD则符合对数正态分布，RTS及RAS的SOC和SOCD均符合正态分布。
2.2 SOC及SOCD的空间自相关性
本研究以最大间距的1/2（35.56 m）为样本变化范围，以最小间距（10 m）为步长，并以平均预测误差（ME）最接近于0，决定系数（R2）和均方根标准预测误差（RMSSE）最接近于1交叉验证模型精度，最优模型如图2所示。
在不考虑各向异性的前提下，除RAS外，CAM和RTS的SOC、SOCD均得到了较好的拟合（表3）。CAM、RTS的SOC最优拟合为球状模型（spherical model），R2分别达0.998、0.432，ME依次为3.268、0.506，RMSSE为0.920、1.057；RAS的SOC最佳拟合模型为线性模型（linear model），R2为0.091，拟合效果较差。3种植被类型SOCD的拟合效果均较好，CAM、RTS及RAS分别符合指数模型（exponential model）、球状模型和线性模型，R2达0.965、0.864和0.996，ME在−0.145 ~ 0.021之间，RMSSE在1.017 ~ 1.059之间。
表 3 不同植被类型SOC及SOCD半方差函数模型及交叉验证Table 3. Semivariogram and cross-validation of SOC and SOCD under different vegetation types指标
Item植被类型
Vegetation
type理论模型
Theoretical model块金值
Nugget
(C0)基台值
Sill
(C0 + C)结构比
Structure ratio
(C0/(C0 + C))/%变程
Range (A0)/
m决定系数
Determination
coefficient (R2)预测误差 Prediction error ME RMSSE SOC CAM 球状模型 Spherical model 25.00 1716.00 1.46 30.29 0.998 −3.268 0.920 RTS 球状模型 Spherical model 58.00 721.30 8.04 17.68 0.432 0.506 1.057 RAS 线性模型 Linear model 2338.32 2338.32 100.00 32.69 0.091 −1.009 1.068 SOCD CAM 指数模型 Exponential model 0.01 4.67 2.14 20.31 0.965 −0.145 1.042 RTS 球状模型 Spherical model 0.58 4.64 12.51 17.44 0.864 0.021 1.059 RAS 线性模型 Linear model 4.00 4.00 100.00 32.69 0.996 −0.065 1.017 从结构比来看，除RAS外，其他植被类型的SOC及SOCD均具有强烈的空间自相关性。CAM与RTS的SOC结构比分别为1.46%、8.04%，SOCD结构比分别为2.14%、12.51%，即CAM < RTS < 25%，结构性因素引起的空间变异大于75%，具有较强的空间依赖性。RAS的SOC及SOCD块金值等于基台值，结构比达100%，属于纯块金方差，空间自相关性较弱，随机性因素控制为主。
从变程来看，SOC、SOCD的空间变异尺度存在差异，反映出不同程度的空间连续性。3种植被类型SOC的变程表现为RAS（32.69 m） > CAM（30.29 m） > RTS（17.68 m），SOCD为RAS（32.69 m） > CAM（20.31 m） > RTS（17.44 m），即SOC、SOCD的空间连续性从RAS、CAM到RTS逐渐变差。除RNS外，CAM和RTS的空间变程均表现为SOC > SOCD，即SOC可能较SOCD具有更大的空间自相关距离，在考察SOC含量时的采样可以适当加大采样间距。
2.3 SOC及SOCD的空间分布
本研究RAS的SOC及SOCD空间上主要受随机因素控制，故不适合做空间插值[23]。CAM及RTS的SOC及SOCD的空间分布如图3所示。总体而言，SOC及SOCD呈斑状或条带状分布，并且同一植被类型的SOC与SOCD空间分布相似。其中CAM的SOC、SOCD高值区集中分布在样地南部和东部边缘，分别在90.29 ~ 219.85 g/kg、8.05 ~ 14.05 kg/m2之间，并且由北部、西部边缘向中部延伸分别形成两个低值分布带，值在49.63 ~ 90.29 g/kg、8.05 ~ 14.50 kg/m2之间。RTS的SOC、SOCD具有相似的高低值分布区，高值区SOC、SOCD分别为56.63 ~ 148.31 g/kg、5.31 ~ 13.09 kg/m2范围，低值区分别在24.01 ~ 56.63 g/kg和2.67 ~ 5.31 kg/m2范围，但与CAM相比，RTS的SOC及SOCD均未表现出明显的边缘效应，而呈高低值交错分布的特征，空间连续性较差。
![]()
图 3 SOC及SOCD的水平分布格局Figure 3. Horizontal distribution patterns of SOC and SOCD under different vegetation types2.4 SOC和SOCD与土壤因子的相关性分析
如图4所示，RDA排序图的典型轴1和轴2解释了SOC、SOCD与土壤性质总体变异的58.2%。土壤含水率、TN在SOC箭头连线上的投影长度最长，表明其与SOC呈较强的正相关性，土壤密度、pH在SOC箭头反向延长线上的投影较长，表明二者与SOC呈较强的负相关性，而土壤机械组成、坡度、TP与SOC的相关性不大。SOCD与SOC呈正相关性，但与土壤各性质的相关性不如SOC显著，主要与TP、TN的关系较为密切。
![]()
图 4 SOC及SOCD与土壤因子相关性的冗余分析排序图SOC. 土壤有机碳；SOCD. 土壤有机碳密度；TN. 全氮；TP. 全磷；Water content. 含水率；Soil density. 土壤密度；Slope. 坡度；Clay. 黏粒；Silt.粉粒；Sand. 砂粒。SOC, soil organic carbon.; SOCD, soil organic carbon density; TN, total nitrogen; TP, total phosphorus.Figure 4. Redundancy analysis on the correlation between SOC and SOCD with soil properties由表4可知，各土壤因子对SOC、SOCD的影响存在一定差异。各土壤因子对SOC、SOCD影响的重要性排序从大到小依次为含水率 > 土壤密度 > pH > TN > 黏粒 > 砂粒 > 粉粒 > 坡度 > TP，除TP对SOC、SOCD的影响未达显著水平外（P > 0.05），其他土壤因子对SOC、SOCD的影响均达极显著水平（P < 0.01），其中含水率、土壤密度和pH因子对SOC、SOCD空间变异的贡献率较高，分别为69.60%、65.70%、53.50%，其次是TN和黏粒，贡献为44.20%和30.10%。表明土壤含水率、土壤密度、pH是影响研究区SOC、SOCD空间分布的关键因素。
表 4 土壤因子解释量及显著性检验Table 4. Explanation rate and significance test of soil factors土壤因子
Soil
factor重要性排序
Importance
ranking因子解释量
Factor explanation
rate/%F P 含水率 Water content 1 69.60 98.40 0.002 土壤密度 Soil density 2 65.70 88.30 0.002 pH 3 53.50 62.10 0.002 全氮 TN 4 44.20 46.60 0.002 黏粒 Clay 5 30.10 27.80 0.002 砂粒 Silt 6 17.50 14.60 0.002 粉粒 Sand 7 15.80 13.00 0.002 坡度 Slope 8 12.50 10.00 0.004 全磷 TP 9 0.20 0.10 0.740 3. 讨 论
3.1 植被类型对SOC和SOCD的影响
本研究中植被类型显著影响了表层0 ~ 10 cm土壤有机碳含量（P < 0.01），与前人研究结果一致[24-25]。表层SOC含量为RAS > CAM > RTS，与植被覆盖度规律一致（表1），即RAS（> 95%） > CAM（95%） > RTS（75%）。这是因为自然生态系统植被枯落物是表层土壤有机碳的重要来源，并与植被覆盖度显著正相关。研究区SOC含量处于较高水平，均值达100.97 g/kg，显著高于全国0 ~ 10 cm土壤水平（24.56 g/kg）[26]。与相似生态类型区比较，CAM的SOC（95.60 g/kg）亦高于青海省河南县杂类草型草甸（74.99 g/kg）[27]和江西省武功山草甸（63.52 g/kg）[28]，两种灌丛SOC的平均含量（103.44 g/kg）亦显著高于燕山北部绣线菊（Spiraea salicifolia）灌丛（37.65 g/kg）和榛子（Corylus heterophylla）灌丛（64.74 g/kg）[29]。这可能与低温抑制酶活性、减缓土壤有机质的矿化分解有关[30]，该区域年均气温仅0.64 ℃，且降水丰富，年降雨达877 mm，有利于表层有机质的积累。SOCD与SOC的表现基本一致，CAM（7.73 kg/m2）、RTS（4.8 kg/m2）和RAS（6.32 kg/m2）的SOCD显著高于全国10 cm土壤厚度的SOCD（1.21 kg/m2）[31]，表明色季拉山高海拔区土壤有机碳储量较丰富，可能成为未来大气CO2浓度升高的潜在碳源。
3.2 SOC及SOCD的空间异质性
本研究小空间尺度上，植被类型影响了表层土壤有机碳的空间分布与变异特征。SOC和SOCD的空间变异半方差函数分析结果表明，拟合度以CAM最佳，其次为RTS，而RAS则较差。这可能是由于小空间尺度上RAS地表地形及植被、枯落物等基本立地条件较为破碎有关，故可能需要增加采样点或缩小采样间距[32]。结构比反映了引起变量空间异质性的主要影响因素，本研究中CAM、RTS的SOC、SOCD结构比均小于13%，由结构性因素引起的空间异质性占总空间异质性的87.49% ~ 98.54%，表明小尺度空间范围，在气候、土壤类型一致的前提下，地面枯落物量、微地形、土壤理化性质等结构性因素是表层SOC、SOCD空间变异的主要原因。而RAS的SOC、SOCD表现为以人为干扰等随机因素控制为主，考虑是采样间距大于其空间变异尺度引起的[33]。
变程反映的是变量的空间自相关范围，在变程内的变量才具有空间自相关性[23]。本研究中CAM和RTS的SOC、SOCD变程均大于取样距离（10 m），表明本研究在该小尺度下设置的采样间距是合理的，可以反映SOC、SOCD的空间异质性。对于RAS而言，10 m可能超出了其SOC、SOCD空间自相关的最大距离，而未表现出空间结构性，反映了SOC、SOCD的尺度效应。本研究中，SOC由空间自相关部分引起的变异占总变异的91.96% ~ 98.54%，表现在30.29 m以下的范围，与辽东山区次生林[34]土壤有机质由空间自相关引起的变异占总变异的65.40%，发生在46.77 m尺度以内相比，本研究SOC在较小尺度内存在较强的空间自相关性，表明色季拉山微生境比较复杂，SOC的空间破碎化严重。
3.3 SOC及SOCD水平分布格局的影响因素分析
SOC空间分布受多种因素的综合影响，其中土壤含水率、微地形是影响该研究区SOC空间分布格局的重要环境变量。CAM样地东、南边缘是SOC、SOCD高值的集中分布区，该边缘与嵩草沼泽化草甸接壤，土壤含水率较高（多数采样点超过100%），在低温通气不良的条件下，微生物活动受限，植物残体和根系不易分解，使土壤有机物质大量积累，符合冗余分析中SOC与土壤含水率极显著正相关的规律。在CAM的中部到北部边缘，存在一条东西方向的沟壑，在地面径流的作用下，导致土壤中可溶性有机碳向地势较低的东部边缘迁移，可能是CAM北部低值分布带形成的主要原因，与石峰[35]关于土壤无机氮的研究结果一致，表明在小尺度空间范围，微地形可能成为土壤养分空间分布的主要驱动因子[36-37]。在本研究中，坡度与SOC、SOCD的相关性较低（P > 0.05），这与Ritchie等[38]的研究结果相异，可能是水、热等条件的差异弱化了小尺度下坡度对SOC、SOCD的影响[39]。
研究区RTS样地为裸地、草本和灌丛镶嵌性分布，可能是其SOC、SOCD空间连续性较CAM差，破碎化程度高的原因。有研究表明土壤有机碳含量与灌丛凋落物生物量呈正相关性[40]，在本研究中，灌丛斑块地上凋落物数量明显高于灌丛间裸地、草地斑块，导致交错分布的不同斑块下表层SOC存在差异，从而表现出空间异质性，与何俊龄[41]的研究结果一致，其研究亦表明金露梅（Potentilla fruticosa）灌丛表层土壤养分含量显著高于灌丛间草地。本研究未对RAS的SOC、SOCD进行空间插值，但其纯块金效应的出现表明其SOC、SOCD的空间连续性可能较差，事实上RAS微地形较复杂，平均坡度达25°，且地面枯落物分布不均匀，厚度0 ~ 10 cm不等，因此有待更小尺度的研究。
土壤密度、pH、TN、机械组成亦是影响SOC及SOCD空间分布格局的重要因素。冗余分析结果表明，土壤密度、pH是影响研究区SOC、SOCD空间变异的关键因子，与SOC、SOCD呈极显著负相关，这一结果与卓志清等[42]、贡璐等[43]人的研究结果一致，SOC含量提高时有利于改善土壤结构，使土壤疏松多孔，降低土壤紧实度。同时在一定范围内，随着土壤酸性增强，微生物活动受限，从而导致碳的积累。土壤中TN大部分来源于土壤有机质，因此与SOC极显著正相关。本研究SOC、SOCD与土壤黏粒、粉粒呈负相关，与砂粒正相关，这是因为黏粒、粉粒含量的降低，有利于提高土壤透气性，从而加快微生物分解释放养分元素，与马文瑛等[44]、Igor等[45]的研究结果相似。
4. 结 论
（1）SOC、SOCD显著受植被类型的影响，本研究区SOC表现为RAS > CAM > RTS，SOCD为CAM > RAS > RTS。
（2）研究区CAM、RTS的SOC和SOCD主要受结构性因素的影响，具有较强的空间自相关性，二者空间自相关的最大距离在17.44 ~ 30.29 m之间，表明本研究采取的地统计采样间隔是合理的。RAS的SOC、SOCD具有纯块金效应，即随采样间隔变化，其方差均为一常量，说明本研究采取的10 m间隔采样超出了RAS 的SOC、SOCD的空间变异尺度。
（3）在水平方向，CAM的SOC、SOCD主要受含水率、微地形影响，而在样地的东部、南部边缘存在高值区，在北部存在东西向的低值分布带。RTS的SOC、SOCD呈高低值交错分布，破碎化程度高，可能与地面覆被物的镶嵌性分布有关。
（4）冗余分析表明，土壤含水率、土壤密度、pH、TN是影响表层SOC、SOCD空间变异的关键要素。
-
![]()
图 1 克拉玛依市（左）和玛纳斯县（右）的样地分布图
Figure 1. Spatial distribution of poplar plots in Karamay City (left) and Manasi County (right) of northwestern China
![]()
图 2 新疆杨腐烂病与林分密度（a）和香浓多样性指数（b）的关系
腐烂病发病率为样地患病树木数量/样地树木总量。下同。Cytospora canker incidence of poplar is the number of diseased trees in the plot/the total number of trees in the plot. Same as below.
Figure 2. Relationship between P. alba var. pyramidalis canker and (a) tree density and (b) Shannon diversity index
![]()
图 3 新疆杨腐烂病与海拔（a）和非寄主比例（b）的关系
Figure 3. Relationship between P. alba var. pyramidalis canker as well as (a) elevation and (b) the proportion of non-hosts
![]()
图 4 黑杨腐烂病与林分密度（a）和香浓多样性指数（b）的关系
Figure 4. Relationship between P. nigra canker and tree density (a) as well as Shannon diversity index (b)
![]()
图 5 黑杨腐烂病与海拔（a）和非寄主比例（b）以及第一活枝高（c）的关系
Figure 5. Relationship between P. nigra canker and elevation (a), the proportion of non-hosts (b) as well as the first live branch (c)
![]()
图 6 新疆杨腐烂病在不同土壤条件及250 m的景观结构中的发生情况
复杂景观表示非林地面积 > 50%，样地是混交林；简单景观则相反。A. 玛纳斯土壤条件；B. 克拉玛依土壤条件。下同。Complex landscape refers to the cumulative proportion of non-forests > 50% and the sample plot is mixed forest, simple landscape is the reverse. A, Manasi soil conditions; B, Karamay soil conditions. Same as below.
Figure 6. P. alba var. pyramidalis canker incidence in landscape structures across two different soil conditions at 250 m spatial scale
![]()
图 7 黑杨腐烂病在不同土壤条件及250 m的景观结构中的发生情况
Figure 7. P. nigra canker incidence in landscape structures across two different soil conditions at 250 m spatial scale
表 1 克拉玛依研究区的土壤理化性质平均变量
Table 1 Mean variables of soil physical and chemical properties at Karamay City research area
项目 Item 土壤理化性质 Physical and chemical properties of soil 0 ~ 10 cm 10 ~ 30 cm 30 ~ 50 cm 荒漠林 Desert forests[45] 有机质 Organic matter/(g·kg− 1) 2.29 1.30 1.80 人工林 Plantation forests[46] 有机质 Organic matter/(g·kg− 1) 4.79 3.98 3.89 有效磷 Available phosphorus/(mg·kg− 1) 5.79 5.02 4.41 全磷 Total phosphorus/ (g·kg− 1) 0.69 0.69 0.58 有效氮 Available nitrogen/(mg·kg− 1) 7.63 6.81 6.54 全氮 Total nitrogen/(g·kg− 1) 0.10 0.10 0.58 pH值 pH value 8.67 8.76 8.71 有机质 Organic matter/(g·kg− 1) 8.41 7.13 6.59 土壤密度 Soil density/(g·cm− 3) 1.62 1.64 1.66 土壤水分 Soil moisture/% 12.08 13.77 13.94 注：表中数据引自参考文献[45−46]。Note: the data in the table is quoted from the references [45−46]. 
下载: 导出CSV
表 2 玛纳斯研究区的土壤理化性质平均变量
Table 2 Mean variables of soil physical and chemical properties at Manasi County research area
土壤理化性质
Physical and chemical properties of soil0~10 cm 0~20 cm 20~30 cm 有机质
Organic matter/(g·kg− 1)11.44 全磷
Total phosphorus/(g·kg− 1)0.53 全氮
Total nitrogen/(g·kg− 1)1.98 全钾
Total potassium/(g·kg− 1)24.22 有效氮
Available nitrogen/(mg·kg− 1)41.75 有效磷
Available phosphorus/(mg·kg− 1)8.53 有效钾
Available potassium/(mg·kg− 1)233.26 pH值 pH value 8.57 土壤水分
Soil moisture/%14.16 17.96 18.86 盐分
Salinity/(g·kg− 1)6 6.66 7.87 注：表中数据引自参考文献[47−49]。Note: the data in the table is quoted from the references [47−49].
下载: 导出CSV
表 3 模型平均后各解释变量对新疆杨腐烂病的影响
Table 3 Results of the model averaging procedure to assess the relationship between P. alba var. pyramidalis canker and explanatory variables
变量
Variable估计值
Estimated value标准误
SEz值
z valueP 重要值
Importance变量所在模型数量
Number of models containing variable第一活枝高 Height of the first live branch − 0.954 9 0.639 1 1.427 0.154 1 64 密度 Density 6.919 1 2.176 1 3.084 0.002** 1 64 香浓多样性指数 Shannon diversity index 3.120 8 0.847 2 3.519 < 0.001*** 1 64 非寄主比例 Proportion of non-hosts − 4.097 2 1.187 4 3.314 < 0.001*** 1 64 海拔 Elevation − 12.379 2 2.135 1 5.536 < 0.001*** 0.97 64 土壤理化性质 Soil physical and chemical property 0.651 7 0.298 0 2.084 0.037* 1 64 景观复杂性 Landscape complexity 2.146 0 0.318 3 6.428 < 0.001*** 0.7 64 注：*表示P < 0.05；**表示 P < 0.01；***表示P < 0.001。下同。Notes: * means P < 0.05; ** means P < 0.01; *** means P < 0.001. Same as below.
下载: 导出CSV
表 4 模型平均后各解释变量对黑杨腐烂病的影响
Table 4 Results of the model averaging procedure to assess the relationship between P. nigra canker and explanatory variables
变量
Variable估计值
Estimated value标准误
SEz 值
z valueP 重要值
Importance变量包含模型数
Number of models containing variable第一活枝高 Height of the first live branch 0.759 4 0.174 7 4.305 < 0.001*** 0.33 64 密度 Density 6.612 5 0.748 7 8.747 < 0.001*** 1 64 香浓多样性指数 Shannon diversity index 2.620 0 0.462 1 5.615 < 0.001*** 0.99 64 非寄主比例 Proportion of non-hosts − 1.752 0 0.417 0 4.161 < 0.001*** 1 64 海拔 Elevation − 2.257 7 0.874 4 2.564 0.010* 1 64 土壤理化性质 Soil physical and chemical properties − 0.432 0 0.118 4 3.615 < 0.001*** 0.68 64 景观复杂性 Landscape complexity 0.325 1 0.164 0 1.964 0.046* 1 64
下载: 导出CSV
-
[1] Vira B, Agarwal B, Jamnadass R H, et al. Forests, trees and landscapes for food security and nutrition[M]. Cambridge: Open Book Publishers, 2015.
[2] Brockerhoff E G, Jactel H, Parrotta J. A. Role of eucalypt and other planted forests in biodiversity conservation and the provision of biodiversity-related ecosystem services[J]. Forest Ecology and Management, 2013, 301: 43−50. doi: 10.1016/j.foreco.2012.09.018
[3] Sukhdev P, Wittmer H, Schröter-Schlaack C, et al. The economics of ecosystems and biodiversity: mainstreaming the economics of nature: a synthesis of the approach, conclusions and recommendations of TEEB[M]. Ginebra (Suiza): Progress Press, 2010.
[4] Ball J, Carle J, Del L A. Contribution of poplars and willows to sustainable forestry and rural development[J/OL]. UNASYLVA-FAO-, 2005, 56 (2): 3 [2019−05−30]. http://www.fao.org/tempref/docrep/fao/008/a0026e/a0026e03.pdf.
[5] Kraeuchi N. Potential impacts of a climate change on forest ecosystems[J]. European Journal of Forest Pathology, 1993, 23(1): 28−50. doi: 10.1111/j.1439-0329.1993.tb00804.x
[6] Wingfield M J, Brockerhoff E G, Wingfield B D, et al. Planted forest health: the need for a global strategy[J]. Science, 2015, 349: 832−836. doi: 10.1126/science.aac6674
[7] Condeso T E, Meentemeyer R K. Effects of landscape heterogeneity on the emerging forest disease sudden oak death[J]. Journal of Ecology, 2007, 95(2): 364−375. doi: 10.1111/j.1365-2745.2006.01206.x
[8] Mundt C C, Sackett K E, Wallace L D. Landscape heterogeneity and disease spread: experimental approaches with a plant pathogen[J]. Ecological Applications, 2011, 21(2): 321−328. doi: 10.1890/10-1004.1
[9] Medeiros H R, Hoshino A T, Ribeiro M C, et al. Landscape complexity affects cover and species richness of weeds in Brazilian agricultural environments[J]. Basic and Applied Ecology, 2016, 17(8): 731−740. doi: 10.1016/j.baae.2016.10.001
[10] Hendrickx F, Maelfait J P, Van W W, et al. How landscape structure, landuse intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes[J]. Journal of Applied Ecology, 2007, 44: 340−351. doi: 10.1111/j.1365-2664.2006.01270.x
[11] Schirmel J, Albrecht M, Bauer P M, et al. Landscape complexity promotes hoverflies across different types of semi-natural habitats in farmland[J]. Journal of Applied Ecology, 2018, 55(4): 1747−1758. doi: 10.1111/1365-2664.13095
[12] Ostfeld R S , Keesing F , Eviner V T . Infectious disease ecology: effects of ecosystems on disease and of disease on ecosystems [M]//McCallum H. Landscape structure, disturbance, and disease dynamics. New Jersey:Princeton University Press, 2008: 100–122.
[13] Padgett G E, Hopkins R L. Landscape epidemiology of Batrachochytrium dendrobatidis in central California[J]. Ecography, 2010, 33(4): 688−697. doi: 10.1111/j.1600-0587.2009.05994.x
[14] With K A. The landscape ecology of invasive spread[J]. Conservation Biology, 2002, 16(5): 1192−1203. doi: 10.1046/j.1523-1739.2002.01064.x
[15] Holdenrieder O, Pautasso M, Weisberg P J, et al. Tree diseases and landscape processes: the challenge of landscape pathology[J]. Trends in Ecology & Evolution, 2004, 19(8): 446−452.
[16] Ostfeld R S, Glass G E, Keesing F. Spatial epidemiology: an emerging (or re-emerging) discipline[J]. Trends in Ecology & Evolution, 2005, 20(6): 328−336.
[17] Batáry P, Báldi A, Szél G, et al. Responses of grassland specialist and generalist beetles to management and landscape complexity[J]. Diversity and Distributions, 2007, 13(2): 196−202. doi: 10.1111/j.1472-4642.2006.00309.x
[18] Rusch A, Bommarco R, Jonsson M, et al. Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale[J]. Journal of Applied Ecology, 2013, 50(2): 345−354. doi: 10.1111/1365-2664.12055
[19] Yang L, Zhang Q, Liu B, et al. Mixed effects of landscape complexity and insecticide use on ladybeetle abundance in wheat fields[J]. Pest Management Science, 2019, 75(6): 1638−1645. doi: 10.1002/ps.5281
[20] Chaplin R, Rourke M E, Blitzer E J, et al. A meta-analysis of crop pest and natural enemy response to landscape complexity[J]. Ecology Letters, 2011, 14(9): 922−932. doi: 10.1111/j.1461-0248.2011.01642.x
[21] Norton L, Johnson P, Joys A, et al. Consequences of organic and non-organic farming practices for field, farm and landscape complexity[J]. Agriculture, Ecosystems & Environment, 2009, 129(1–3): 221−227.
[22] Winqvist C, Bengtsson J, Aavik T, et al. Mixed effects of organic farming and landscape complexity on farmland biodiversity and biological control potential across Europe[J]. Journal of Applied Ecology, 2011, 48(3): 570−579. doi: 10.1111/j.1365-2664.2010.01950.x
[23] Plantegenest M, Le M C, Fabre F. Landscape epidemiology of plant diseases[J]. Journal of the Royal Society Interface, 2007, 4(16): 963−972. doi: 10.1098/rsif.2007.1114
[24] Christersson L. Poplar plantations for paper and energy in the south of Sweden[J]. Biomass and Bioenergy, 2008, 32(11): 997−1000. doi: 10.1016/j.biombioe.2007.12.018
[25] Fang S, Xue J, Tang L. Biomass production and carbon sequestration potential in poplar plantations with different management patterns[J]. Journal of Environmental Management, 2007, 85(3): 672−679. doi: 10.1016/j.jenvman.2006.09.014
[26] Lovarelli D, Fusi A, Pretolani R, et al. Delving the environmental impact of roundwood production from poplar plantations[J]. Science of the Total Environment, 2018, 645: 646−654. doi: 10.1016/j.scitotenv.2018.06.386
[27] Wang Y, Bai G, Shao G, et al. An analysis of potential investment returns and their determinants of poplar plantations in state-owned forest enterprises of China[J]. New Forests, 2014, 45(2): 251−264. doi: 10.1007/s11056-014-9406-z
[28] Ma Z, Zhu J, Sun Z, et al. The influences of biotic and abiotic factors on the occurrence and severity of poplar canker disease in Qingfeng County, China and the management implications[J]. Journal of Forestry Research, 2015, 26(4): 1025−1034. doi: 10.1007/s11676-015-0122-0
[29] Ennos R A. Resilience of forests to pathogens: an evolutionary ecology perspective[J]. Forestry: An International Journal of Forest Research, 2014, 88(1): 41−52.
[30] Steenackers J, Steenackers M, Steenackers V, et al. Poplar diseases, consequences on growth and wood quality[J]. Biomass and Bioenergy, 1996, 10(5–6): 267−274. doi: 10.1016/0961-9534(95)00121-2
[31] Wang G, Dong Y, Liu X, et al. The current status and development of insect-resistant genetically engineered poplar in China[J/OL]. Frontiers in Plant Science, 2018 [2019−05−30]. http://people.dyu.edu.tw/paper/9805530_e.pdf.
[32] Yi H W, Chi Y J. Biocontrol of cytospora canker of poplar in north‐east China with trichoderma longibrachiatum[J]. Forest Pathology, 2011, 41(4): 299−307. doi: 10.1111/j.1439-0329.2010.00704.x
[33] 潘峰, 田长彦, 邵峰, 等. 新疆克拉玛依市生态敏感性研究[J]. 地理学报, 2011, 66(11):1497−1507. doi: 10.11821/xb201111006 Pan F, Tian C Y, Shao F, et al. Study on the ecological sensitivity of Karamay City in Xinjiang[J]. Acta Geographica Sinica, 2011, 66(11): 1497−1507. doi: 10.11821/xb201111006
[34] 岳璐. 干旱地区节水型公路植物景观设计规划研究:以克拉玛依市为例[J]. 科学技术与工程, 2017, 17(7):299−302, 308. Yue L. Study on plant landscape design and planning of water-saving highway in arid area: Taking Karamay City as an example[J]. Science Technology and Engineering, 2017, 17(7): 299−302, 308.
[35] 钟玉婷, 范子昂, 刘新春, 等. 克拉玛依大气降水化学组分及来源分析[J]. 干旱区地理, 2019, 42(5):1038−1047. Zhong Y T, Fan Z, Liu X C, et al. Chemical Characteristics and Source Assessment of Atmospheric Precipitation in Karamay[J]. Arid Area Geography, 2019, 42(5): 1038−1047.
[36] 黄聪. 克拉玛依人工林景观格局变化与气候因子关系分析[D]. 乌鲁木齐: 新疆大学, 2010. Huang C. Analysis of the relationship between landscape pattern change and climatic factors of Karamay plantation area[D]. Urumchi: Xinjiang University, 2010.
[37] 轩俊伟, 盛建东. 2001—2017年新疆玛纳斯河流域植被NDVI时空变化特征[J]. 林业资源管理, 2018, 6(6):30−37. Xuan J W, Sheng J D. Analysis on the spatial-temporal variation of NDVI in the Manas River Watershed of Xinjiang from 2001 to 2017[J]. Forest Resources Management, 2018, 6(6): 30−37.
[38] 黄犁, 徐丽萍. 玛纳斯河流域绿洲时空演变及其景观格局变化[J]. 干旱区研, 2019, 36(5):1261−1269. Huang L, Xu L P. An analysis of spatial and temporal evolution of oasis in the middle and lower reaches of Manas River Basin[J]. Arid Zone Research, 2019, 36(5): 1261−1269.
[39] 李义玲, 乔木, 杨小林, 等. 干旱区典型流域近30年土地利用/土地覆被变化的分形特征分析: 以玛纳斯河流域为例[J]. 干旱区地理, 2008(1):75−81. Li Y L, Qiao M, Yang X L, et al. Analysis of fractal characteristics of land use/land cover change in typical watersheds in arid areas in recent 30 years: taking the Manas River Basin as an example[J]. Arid Land Geography, 2008(1): 75−81.
[40] Meentemeyer R K, Condeso T E. Effects of landscape heterogeneity on the emerging forest disease sudden oak death[J]. Phytopathology, 2006, 96(6): 364−375.
[41] Ito S, Nakayama R, Buckley G P. Effects of previous land-use on plant species diversity in semi-natural and plantation forests in a warm-temperate region in southeastern Kyushu, Japan[J]. Forest Ecology and Management, 2004, 196(2–3): 213−225. doi: 10.1016/j.foreco.2004.02.050
[42] Damascena J G, Leite G L D, Silva F W S, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance[J]. Ecography, 2013, 36(1): 27−46. doi: 10.1111/j.1600-0587.2012.07348.x
[43] Shannon C. A mathematical theory of communication[J]. Bell System Technical Journal, 1948, 27: 379−656. doi: 10.1002/j.1538-7305.1948.tb01338.x
[44] Simpson E H. Measurement of diversity[J]. Nature, 1949, 163: 688. doi: 10.1038/163688a0
[45] 张文俊, 马珂, 师庆东, 等. 克拉玛依人工杨树碳汇林对土壤有机碳储量的影响[J]. 新疆农业科学, 2012, 49(5):856−861. Zhang W J, Ma K, Shi Q D, et al. The study of the influence poplar plantation carbon sink[J]. Xinjiang Agricultural Sciences, 2012, 49(5): 856−861.
[46] 张毓涛, 郭树芳, 张新平, 等. 克拉玛依人工林生物量与土壤理化性质[J]. 干旱区研究, 2015, 32(2):402−409. Zhang Y T, Guo S F, Zhang X P, et al. Biomass and soil physical and chemical properties[J]. Arid Zone Research, 2015, 32(2): 402−409.
[47] 阿依古丽·买买提, 吉力力·阿不都外力, 葛拥晓. 玛纳斯河流域绿洲土壤养分空间变异特征与格局[J]. 干旱区资源与环境, 2013, 27(9):153−159. Mamat A, Abuduwai J, Ge Y X. Spatial variability and pattern of soil nutrients in the oasis of Manas River Watershed[J]. Journal of Arid Land Resources and Environment, 2013, 27(9): 153−159.
[48] 张丽, 才日玛, 盛建东. 玛纳斯县农田土壤养分变化分析[J]. 新疆师范大学学报(自然科学版), 2014, 33(1):20−26. Zhang L, Cai R M, Sheng J D. Analysis of soil nutrient changes in farmland in Manas County[J]. Journal of Xinjiang Normal University (Natural Science Edition), 2014, 33(1): 20−26.
[49] 沈浩, 吉力力·阿不都外力. 玛纳斯河流域农田土壤水盐空间分布特征及影响因素[J]. 应用生态学报, 2015, 26(3):769−776. Shen H, Jili A W. Spatial distribution of soil moisture and salinity and their influence factors in the farmland of Manas River catchment, Northwest China[J]. Journal of Applied Ecology, 2015, 26(3): 769−776.
[50] Schmiedel I, Bergmeier E, Culmsee H. Plant species richness patterns along a gradient of landscape modification intensity in Lower Saxony, Germany[J]. Landscape and Urban Planning, 2015, 141: 41−51. doi: 10.1016/j.landurbplan.2015.03.009
[51] Schulz C, Koch R, Cierjacks A, et al. Land change and loss of landscape diversity at the Caatinga phytogeographical domain: Analysis of pattern-process relationships with MODIS land cover products (2001–2012)[J]. Journal of Arid Environments, 2017, 136: 54−74. doi: 10.1016/j.jaridenv.2016.10.004
[52] Burnham K P, Anderson D R, Huyvaert K P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons[J]. Behavioral Ecology and Sociobiology, 2011, 65(1): 23−35. doi: 10.1007/s00265-010-1029-6
[53] Nakagawa S, Freckleton R P. Model averaging, missing data and multiple imputation: a case study for behavioural ecology[J]. Behavioral Ecology and Sociobiology, 2011, 65(1): 103−116. doi: 10.1007/s00265-010-1044-7
[54] Bates D, Mächler M, Bolker B S, et al. Fitting linear mixed-effects models using lme4[Z/OL]. Journal of Statistical Software, 2015 [2019−05−30]. https://www.jstatsoft.org/issue/view/v067.
[55] Barton K. MuMIn: Multi-Model inference[J]. R Package Version, 2014, 1(10): 5−25.
[56] Fatehi P, Damm A, Leiterer R, et al. Tree density and forest productivity in a heterogeneous alpine environment: insights from airborne laser scanning and imaging spectroscopy[J]. Forests, 2017, 8(6): 212. doi: 10.3390/f8060212
[57] Fatehi P, Damm A, Schaepman M, et al. Estimation of alpine forest structural variables from imaging spectrometer data[J]. Remote Sensing, 2015, 7(12): 16315−16338. doi: 10.3390/rs71215830
[58] Shibata S. Effect of density control on tree growth at ecological tree planting sites in Japan[J]. Landscape and Ecological Engineering, 2006, 2(1): 13−19. doi: 10.1007/s11355-006-0031-7
[59] Gurr G M, Wratten S D, Landis D A, et al. Habitat management to suppress pest populations: progress and prospects[J]. Annual Review of Entomology, 2017, 62: 91−109. doi: 10.1146/annurev-ento-031616-035050
[60] Bolliger J, Schweizer W. Swiss forests and climate change: comparison of simulated quantitative vegetation models[J]. Schweizerische Zeitschrift fur Forstwesen, 2002, 153(5): 167−175. doi: 10.3188/szf.2002.0167
[61] Van Drunen S G, McCune J L, Husband B C. Distribution and environmental correlates of fungal infection and host tree health in the endangered American chestnut in Canada[J]. Forest Ecology and Management, 2018, 427: 60−69. doi: 10.1016/j.foreco.2018.05.051
[62] Jactel H, Brockerhoff E G. Tree diversity reduces herbivory by forest insects[J]. Ecology Letters, 2007, 10(9): 835−848. doi: 10.1111/j.1461-0248.2007.01073.x
[63] Guyot V, Castagneyrol B, Vialatte A, et al. Tree diversity limits the impact of an invasive forest pest[J]. PLoS One, 2015, 10(9), e0136469 [2019−11−05]. https://doi.org/10.1371/journal.pone.0136469.
[64] Jonsson, M, Straub C S, Didham R K, et al. Experimental evidence that the effectiveness of conservation biological control depends on landscape complexity[J]. Journal of Applied Ecology, 2015, 52(5): 1274−1282. doi: 10.1111/1365-2664.12489
[65] Vasseur C, Joannon A, Aviron S, et al. The cropping systems mosaic: how does the hidden heterogeneity of agricultural landscapes drive arthropod populations[J]. Agriculture, Ecosystems & Environment, 2013, 166: 3−14.
[66] Tscharntke T, Bommarco R, Clough Y, et al. Reprint of “Conservation biological control and enemy diversity on a landscape scale”[J]. Biological Control, 2008, 45(2): 238−253. doi: 10.1016/S1049-9644(08)00082-0
[67] Bianchi F J, Booij C J H, Tscharntke T. Sustainable pest regulation in agricultural landscapes: a review on landscape composition, biodiversity and natural pest control[J]. Proceedings of the Royal Society B: Biological Sciences, 2006, 273: 1715−1727. doi: 10.1098/rspb.2006.3530
[68] Tillman P G, Smith H, Holland J. Cover crops and related methods for enhancing agricultural diversity and conservation biocontrol: Successful case studies[J/OL]. Book Chapter, 2012, 309−327 [2019−10−30]. https://www.ars.usda.gov/research/publications/publication/?seqNo115=263004.
[69] 陈接华, 王绍明, 曹国栋, 等. 玛纳斯河流域不同地貌和植被类型下土壤物理性质研究[J]. 新疆农业科学, 2012, 49(2):354−361. Chen J H, Wang S M, Cao G D, et al. Study on soil physical properties under different landforms and vegetation types in manas river watershed[J]. Xinjiang Agricultural Science, 2012, 49(2): 354−361.
[70] Dai L, Zhao W, Shao G, et al. The progress and challenges in sustainable forestry development in China[J]. International Journal of Sustainable Development & World Ecology, 2013, 20(5): 394−403.
[71] Ryan M G, Binkley D, Fownes J H. Age-related decline in forest productivity: pattern and process[J]. Advances in Ecological Research, 1997, 27: 213−262.
[72] Castagneyrol B, Jactel H, Vacher C, et al. Effects of plant phylogenetic diversity on herbivory depend on herbivore specialization[J]. Journal of Applied Ecology, 2014, 51(1): 134−141. doi: 10.1111/1365-2664.12175
[73] Iverson A L, Marín L E, Ennis K K, et al. Do polycultures promote win-wins or trade-offs in agricultural ecosystem services? A meta-analysis[J]. Journal of Applied Ecology, 2014, 51(6): 1593−1602. doi: 10.1111/1365-2664.12334
[74] Castagneyrol B, Jactel H. Unraveling plant-animal diversity relationships: a meta-regression analysis[J]. Ecology, 2012, 93(9): 2115−2124. doi: 10.1890/11-1300.1
[75] Guyot V, Castagneyrol B, Vialatte A, et al. Tree diversity reduces pest damage in mature forests across Europe[J]. Biology Letters, 2016, 12(4): 20151037. doi: 10.1098/rsbl.2015.1037
[76] Rega C, Bartual A M, Bocci G, et al. A pan-European model of landscape potential to support natural pest control services[J]. Ecological Indicators, 2018, 90: 653−664. doi: 10.1016/j.ecolind.2018.03.075
[77] Brockerhoff E G, Kimberley M, Liebhold A M, et al. Predicting how altering propagule pressure changes establishment rates of biological invaders across species pools[J]. Ecology, 2014, 95(3): 594−601. doi: 10.1890/13-0465.1
[78] Haack R A, Britton K O, Brockerhoff E G, et al. Effectiveness of the International Phytosanitary Standard ISPM No. 15 on reducing wood borer infestation rates in wood packaging material entering the United States[J/OL]. PLoS One, 2014, 9(5), e96611 [2019−05−30]. https://doi.org/10.1371/journal.pone.0096611.
[79] Roy B A, Alexander, H M, Davidson J, et al. Increasing forest loss worldwide from invasive pests requires new trade regulations[J]. Frontiers in Ecology and the Environment, 2014, 12(8): 457−465. doi: 10.1890/130240
-
期刊类型引用(1)
1. 杨冲，张子木，朱麒轩，杜瑶，李黎，罗斌. 基于中密度纤维板磨削的定组态砂带寿命研究. 林业机械与木工设备. 2023(03): 62-66+75 . 
百度学术
其他类型引用(4)
计量
- 文章访问数: 2482
- HTML全文浏览量: 666
- PDF下载量: 90
- 被引次数: 5
