A spatially explicit analysis to extrapolate carbon fluxes in upland tundra where permafrost is thawing

One of the most important changes in high‐latitude ecosystems in response to climatic warming may be the thawing of permafrost soil. In upland tundra, the thawing of ice‐rich permafrost can create localized surface subsidence called thermokarst, which may change the soil environment and influence ecosystem carbon release and uptake. We established an intermediate scale (a scale in between point chamber measurements and eddy covariance footprint) ecosystem carbon flux study in Alaskan tundra where permafrost thaw and thermokarst development had been occurring for several decades. The main goal of our study was to examine how dynamic ecosystem carbon fluxes [gross primary production (GPP), ecosystem respiration (R_eco), and net ecosystem exchange (NEE)] relate to ecosystem variables that incorporate the structural and edaphic changes that co‐occur with permafrost thaw and thermokarst development. We then examined how these measured ecosystem carbon fluxes responded to upscaling. For both spatially extensive measurements made intermittently during the peak growing season and intensive measurements made over the entire growing season, ecosystem variables including degree of surface subsidence, thaw depth, and aboveground biomass were selected in a mixed model selection procedure as the ‘best’ predictors of GPP, R_eco, and NEE. Variables left out of the model (often as a result of autocorrelation) included soil temperature, moisture, and normalized difference vegetation index. These results suggest that the structural changes (surface subsidence, thaw depth, aboveground biomass) that integrate multiple effects of permafrost thaw can be useful components of models used to estimate ecosystem carbon exchange across thermokarst affected landscapes.     

基于GIS的基因表达图谱模型的建立与应用

本文在合理假设的基础上,根据2010年全国研究生数学建模竞赛A题提供的数据及相关信息,在GIS的支持下构建了基因表达图谱模型(简称GEPM),并对其进行空间分析,从而达到对肿瘤识别信息基因提取的目的。结果表明,在参与分析的1 991个基因中,有7个基因可以作为肿瘤识别的信息基因;通过GIS技术构建GEPM对于肿瘤的识别与诊断是可行的。因此,通过本文的研究为基因的识别和研究提供了新的方法。     

黄土高原丘陵沟壑小流域土壤水分垂直分布变异特征及影响因子

以地统计学的半变异函数为分析工具,分析了黄土高原丘陵沟壑区小流域土壤水分在垂直方向的空间变异特征以及土地利用类型和地形等因子对其的影响.结果表明:1)球状模型可以很好地拟合土壤水分在垂直方向的半变异函数曲线,其存在强烈或中等程度的空间自相关,变程范围从2～5 m不等;2)果园对土壤水分含量的影响主要表现在1～2 m深度,5月份含量最高,且分布均匀,8月份由于气温和叶面蒸腾作用,水分含量最低.坡耕地和梯田的水分含量都较高,垂直变化不明显,梯田的土壤水分含量最低月份是9月,比坡耕地晚1个月,这是因为所种作物的主要生长季节为9月份,这期间消耗水分较多而造成的.林地由于根系发达,对土壤水分垂直方向的变化的影响比较大,变化为先增大、再减小、最后再增大且分布趋于平缓.灌木林的土壤水分含量整体较低,主要变化深度范围集中在0～2 m;草地的土壤水分含量较高,垂直变化的深度范围集中在1 m以内;3)坡度、坡向地形因子和土壤水分的垂直方向变异特征没有呈现明显的相关性.     

城市景观多样性的空间尺度分析——以上海市外环线以内区域为例

景观生态学研究的就是某一空间尺度范围内的景观格局与生态过程。因为景观格局与生态过程中存在的尺度多样性 ,导致尺度成为理解景观格局和生态过程相互作用的关键 ,其已经成为景观生态学的一个重要概念 ,但是由于理论和方法的限制 ,对景观生态学的尺度研究还不够 ,特别是景观格局综合性指标在不同幅度上的变化特征和效应。在 GIS与 RS技术支持下 ,采用基准分辨率为 5 m的 SPOT遥感图像作为数据源 ,对不同幅度下的城市景观多样性的空间分布格局进行了分析 ,并进一步利用半变异函数对其空间异质性进行定量描述。结论揭示 :随着空间尺度的增加 ,景观多样性程度也不断增加 ,另外多样性的空间分布格局也具有显著变化 ,由于受城市发展历史和目前城市扩展方向的影响 ,多样性在总体上是不平衡的 ,尺度越大 ,不平衡越明显 ;不同尺度下景观多样性空间格局的变化 ,与城市景观的特点和城市景观的功能息息相关 ,不过其受经济效益和社会文化效益的影响更大 ;随着尺度增加由于掩盖了更小尺度上的变异 ,导致块金效应增强 ,空间自相关部分对系统总的变异则明显下降 ;景观多样性具有尺度依赖性 ,可以说景观多样性也是尺度的函数 ,在不同的尺度上 ,结果差异显著 ,所以在景观生态学的研究中绝对不能忽略尺度对格局的影响     

采伐干扰对华北落叶松细根生物量空间异质性的影响

以华北落叶松天然林为研究对象,选择采伐干扰林分(样地A)和未采伐干扰林分(样地B),利用根钻法分3层(0—10cm,10—20cm,20—30cm)获取各径级细根(≤1mm、1—2mm、2—5mm3级活细根,≤2mm死亡细根)生物量数据。采用地统计学变异函数和经典统计相结合的数据分析方法对采伐干扰造成的细根生物量空间异质性的变化进行定量研究。主要研究结果如下:采伐干扰林分样地A各经级细根生物量均值减少;同一土层相同径级细根生物量样地A与样地B相比差异显著(P<0.05);不同土层的细根生物量异质性具有显著差别(P<0.05)。0—10cm土层,未采伐干扰林分≤1mm细根生物量呈现较明显的空间自相关变异,采伐干扰林分则表现为随机性变异特征,采伐干扰导致≤1mm细根生物量空间分布特征更加复杂(分维数D=1.978);10—20cm土层,采伐干扰林分各径级细根生物量异质性程度明显降低,只有未采伐干扰林分的5.4%—88.9%。20—30cm土层,未采伐干扰林分≤1mm细根生物量在较小尺度范围(<2.9m)表现出明显的空间自相关变异(结构方差比86.1%),受采伐干扰林分各径级细根生物量异质性程度只有未采伐干扰林分的8.9%—45.9%,且呈现随机性变异。各径级细根生物量空间异质性的垂直分异均表现为随土层深度的增加异质性强度明显降低。     

Upscaling leaf area index in an Arctic landscape through multiscale observations

Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site‐specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub‐Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine‐scale harvest LAI–NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI–NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI–NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of ～0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground‐based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI–LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio‐temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.     

Using Geostatistical Analysis to Evaluate the Presence of Rotylenchulus reniformis in Cotton Crops in Brazil: Economic Implications

In recent years, the productivity of cotton in Brazil has been progressively decreasing, often the result of the reniform nematode Rotylenchulus reniformis. This species can reduce crop productivity by up to 40%. Nematodes can be controlled by nematicides but, because of expense and toxicity, application of nematicides to large crop areas may be undesirable. In this work, a methodology using geostatistics for quantifying the risk of nematicide application to small crop areas is proposed. This risk, in economic terms, can be compared to nematicide cost to develop an optimal strategy for Precision Farming. Soil (300 cm³) was sampled in a regular network from a R. reniformis-infested area that was a cotton monoculture for 20 years. The number of nematodes in each sample was counted. The nematode number per volume of soil was characterized using geostatistics, and 100 conditional simulations were conducted. Based on the simulations, risk maps were plotted showing the areas where nematicide should be applied in a Precision Farming context. The methodology developed can be applied to farming in countries that are highly dependent on agriculture, with useful economic implications.     

人类群体遗传空间结构的"克立格"模型

通过将“克立格”技术应用于人类群体遗传学领域,构建了人类群体遗传空间结构的“克立格”模型,并论述了其原理和计算方法。以HLA-A基因座为例,应用“克立格”模型,定量分析了中国人群HLA-A基因座的空间遗传异质性;对HLA-A基因频率的空间数据矩阵进行了主成分分析,进而定义了人类群体遗传结构的综合遗传测度(SPC),绘制了综合遗传测度和主成分(PC)的“克立格”地图,分析了其群体遗传空间结构特性。与其他空间插值或平滑方法相比,人类群体遗传空间结构的“克立格”模型具有明显优点：1)“克立格”估计以空间遗传变异函数模型为基础,在绘制空间遗传结构地图之前,可利用变异函数模型定量分析所研究基因座(或多基因座)的空间遗传异质性;2)“克立格”插值方法是真正意义上的无偏估计模型,它利用待估区域周围的已知群体遗传调查点数据,并充分考虑调查点的空间影响范围,给出待估区域的最优估计值;3)“克立格”模型允许估计插值误差,这种插值误差既可用于评价空间估计效果,又可通过绘制误差地图指导在误差过高的地点增加新的群体遗传调查样本点,以优化估计效果。然而,人类群体遗传空间结构的“克立格”模型也存在一定缺点：1)若不能用任何理论遗传变异函数模型拟合观察遗传变异函数值,则不能建立“克立格”模型;2)若理论遗传变异函数的拟合优度很低,则据此建立的“克立格”模型的估计标准差在整个空间范围内会很大,此时“克立格”模型不适用于估计群体遗传空间结构。出现上述两种情形时,应选用不考虑空间相关性的空间随机插值方法绘制群体遗传结构地图,如基因绘图软件中的Cavalli-Sforza方法,反向距离加权法和条样函数插值法等。     

毛乌素沙漠-黄土高原过渡带土壤养分空间异质性

毛乌素沙漠-黄土高原过渡带土壤养分的空间异质性和生态学过程,对沙荒地整治的机理研究具有重要的意义。以毛乌素沙漠-黄土过渡带为研究区,结合布点取样和室内分析,运用经典统计学和地统计学方法对其全氮、有效磷、速效钾含量的空间异质性进行分析。结果表明,(1)土壤全氮、有效磷、速效钾的平均含量分别为0.39 g/kg、9.65 mg/kg和106.84 mg/kg。3种养分的变异系数为40.54%—84.62%,均属于中等程度变异,其中全氮变异系数最大,速效钾变异系数最小。(2)半方差分析结果显示,3种养分空间变异性的最佳拟合模型均为高斯模型,空间自相关性均随着滞后距离的增加而呈下降趋势。3种养分空间变异性的块金值/基台值比值在0.09%—32.82%,全氮、有效磷具有强烈的空间相关性,结构性因素对变异起主导作用;速效钾具有中等强度的空间相关性,结构性因素和随机性因素共同对变异起主导作用。(3)克里金插值图显示3种养分含量均表现为随着地势的降低而逐渐升高的趋势,全氮含量整体呈斑点状分布,插值图较破碎,有效磷和速效钾含量整体呈条带状分布,连续性较好。(4)毛乌素沙漠-黄土过渡带土壤养分的空间变异性与地形、地貌、植物分布以及非自然因素都有关,但是以地形因素的影响为主。开展沙漠-黄土过渡带土壤养分空间异质性特征研究,为开展沙荒地整治工程,生态系统修复提供了理论依据。