Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events

Prognostic models based on survival data frequently make use of the Cox proportional hazards model. Developing reliable Cox models with few events relative to the number of predictors can be challenging, even in low-dimensional datasets, with a much larger number of observations than variables. In such a setting we examined the performance of methods used to estimate a Cox model, including (i) full model using all available predictors and estimated by standard tech-niques, (ii) backward elimination (BE), (iii) ridge regression, (iv) least absolute shrinkage and selec-tion operator (lasso), and (v) elastic net. Based on a prospective cohort of patients with manifest coronary artery disease (CAD), we performed a simulation study to compare the predictive accu-racy, calibration, and discrimination of these approaches. Candidate predictors for incident cardio-vascular events we used included clinical variables, biomarkers, and a selection of genetic variants associated with CAD. The penalized methods, i.e., ridge, lasso, and elastic net, showed a compara-ble performance, in terms of predictive accuracy, calibration, and discrimination, and outperformed BE and the full model. Excessive shrinkage was observed in some cases for the penalized methods, mostly on the simulation scenarios having the lowest ratio of a number of events to the number of variables. We conclude that in similar settings, these three penalized methods can be used interchangeably. The full model and backward elimination are not recommended in rare event scenarios.     

基于实测高光谱与Sentinel-2B数据的银北土壤Na+含量估测

为了探讨不同传感器对土壤Na⁺含量的估测能力,本研究以宁夏银北地区典型样点土壤实测光谱和Sentinel-2B影像光谱为对象,运用逐步回归(SR)和主成分回归分析(PCA)方法对光谱数据进行敏感参量筛选,然后采用偏最小二乘回归(PLSR)、支持向量机(SVM)和反向传播神经网络模型(BPNN)分别建立实测光谱和影像数据的土壤Na⁺含量估算模型。结果表明: 除Band9外,实测重采样数据与影像数据呈极显著相关。基于SR筛选方式建立的模型估算精度普遍高于PCA(SVM模型除外),PCA-SVM模型为影像最佳Na⁺含量估算模型,预测精度为0.792;SR-BPNN模型为实测最佳Na⁺含量估算模型,预测精度达到0.908。经重采样实测光谱模型校正后的SR-PLSR影像光谱土壤Na⁺含量估算模型精度从0.481提高到0.798,有效提高了较大尺度下的土壤Na⁺含量估算精度。本研究实现了遥感监测土壤Na⁺含量由点向面的空间转换,为Sentinel-2B影像监测盐渍化土壤Na⁺含量提供了科学参考。     

陕北黄土高原植被净初级生产力的估算

基于MODIS和地面气象数据,利用改进的CASA模型,模拟分析了2005年陕北黄土高原地区的植被净初级生产力(NPP)及其时空分布.结果表明:1)根据生态生理过程模型针对不同土地覆被类型选择不同的月平均最大光能利用率,比传统CASA模型中使用固定的全球月平均最大光能利用率进行NPP估算,更符合陕北黄土高原地区的实际情况;在估算植被参数时引入植被覆盖分类,以及利用陕北黄土高原2005年时序NDVI进行土地覆被分类的同时,结合1:100万中国植被图和实地调查情况对分类结果进行修正,可提高分类的精度,从而提高模型估算的精度.2)通过不同模型之间和与陕北部分地区实际调查数据进行比较,显示改进后的CASA模型对区域陆地植被NPP的模拟效果较好,可应用于陕北黄土高原乃至周边地区NPP的计算中.3)2005年陕北黄土高原植被净第一性生产量估计值为4.76×10~(13) g C,约占全国总NPP的1.5%,植被平均NPP为447.3 g C·m~(-2)·a~(-1),高于1992-2000年全国陆地NPP平均值323.8 g C·m~(-2)·a~(-1).4)在NPP的空间分布上,总体趋势是由东南向西北递减,其中最高值出现在东南部的黄龙山次生林区(1087g C·m~(-2)·a~(-1));西北部的荒漠植被覆盖度极低,平均NPP仅为205.0 g C·m~(-2)·a~(-1).5)陕北黄土高原NPP的季节变化明显,其中4月中旬至10月中旬6个月生长季时间里的NPP可占到全年的91.5%,而7月中旬至8月中旬间该区的净初级生产力达到年内的极大值,可占全年的37.8%.

Abstract：

Based on the data from MODIS (Moderate-resolution Imaging Speetroradiometer) and meteorological observatories, and by using improved CASA (Carnegie-Ames-Stanford Approach) model, the vegetation net primary productivity (NPP) and its spatiotemporal distribution on the North Shaanxi Loess Plateau in 2005 were simulated and analyzed. Comparing with the traditional CASA model which only uses a universal mean annual maximum light use efficiency (LUE), the estimated regional NPP by the improved CASA model was more precise, because this improved model used the LUE parameters of different vegetation covers. The detailed land cover classifica-tion also contributed to the increase of the precision via introducing the time-series Normalized Different Vegetation Index (NDVI) and ground survey data to modify and adjust the original clas-sification system based on vegetation map (1: 1000000). The testing of the simulation results from different models with the ground survey data in North Shaanxi showed that the estimation by the modified CASA model was much closer to the real survey data, implying the potential practi-cal significance of this model in estimating the vegetation NPP in North Shaanxi Loess Plateau and its adjacent areas. In 2005, the NPP in North Shaanxi was estimated as 4. 76×1013 g C, ac-counting for about 1.5% of China' s terrestrial total NPP, and the mean NPP was 447.3 g C·m~(-2)·a~(-1), being much higher than that of China' s terrestrial vegetation (323.8 g C·m~(-2)·a~(-1)) in 1982-2000. The spatial distribution pattern of the vegetation NPP showed an apparently declining trend from the southeast to the northwest, with the highest value of 1087 g C·m~(-2)a~(-1) occurred in the broadleaved-and conifer-mixed forests of Huanglong Mountain in southeast part of the region. The mean NPP of desert vegetation in the whole region was the lowest, only about 205.0 g C·m~(-2) ·a~(-1). An obvious seasonal variation of the NPP was observed. The NPP in growth season (from April to October) took about 91.5% of the total in the year, and the peak occurred in mid-July to mid-August, amounting to 37.8% of the total.     

基于HJ1B和ALOS/PALSAR数据的森林地上生物量遥感估算

森林地上生物量的精确估算能够减小碳储量估算的不确定性。为了探寻一种有效地提高森林生物量估算精度的方法,探讨了基于遥感物理模型和经验统计模型估算山地森林地上生物量的方法。首先,基于Li-Strahler几何光学模型和多元前向模式(MFM)进行模型模拟,结合查找表算法(LUT)从多光谱图像HJ1B估算贺兰山研究区的森林地上生物量。其次,采用统计方法建立了2种回归模型:(1)多光谱图像HJ1B进行混合像元分解(SMA),并与雷达图像ALOS/PALSAR进行图像融合建立生物量回归模型;(2)雷达图像ALOS/PALSAR后向散射系数和实测生物量建立了生物量回归模型。用实测数据对3种算法估算结果进行精度验证。研究结果表明:采用几何光学模型和MFM算法估算的森林地上生物量精度最好(决定系数R2=0.61,均方根误差RMSE=8.33 t/hm2,P0.001),其估算地上生物量与实测值一致性较好,估算生物量精度略优于SMA估算的精度(R2=0.60,RMSE=9.417 t/hm2);ALOS/PALSAR多元回归估算的精度最差(R2=0.39,RMSE=14.89 t/hm2)。由此可见,采用几何光学模型和混合像元分解SMA适合估算森林地上生物量,利用这2种方法进行森林地上生物量遥感监测研究具有一定的应用潜力。     

湿地土壤全氮和全磷含量高光谱模型研究

氮磷是湿地生态系统土壤中的重要营养元素,其对湿地植被生长、湿地生态系统生产力、区域富营养化变化、湿地环境生态净化功能等具有重要的影响作用。研究氮磷营养物质在湿地土壤中的分布变化特征,对湿地生态系统评估、恢复和管理具有重要的意义。以中国高纬度地区面积最大的滨海芦苇湿地——盘锦湿地为研究区,采用不同建模方法(再抽样多元逐步回归模型bootstrap SMLR和再抽样偏最小二乘回归模型bootstrap PLSR)和光谱变换技术(包络线去除CR、光谱一阶微分FD和光谱倒数的对数LR),分别建立了湿地土壤全氮和全磷含量的估算模型。基于湿地土壤实测光谱,模拟高光谱Hyperion数据和多光谱TM数据,在此基础上进行湿地土壤营养元素含量估算。对比所建反演模型的估算精度,探讨高光谱遥感技术对湿地土壤营养元素组分的估算能力和适用性。研究结果表明:bootstrap PLSR相比于bootstrap SMLR建模方法,其对研究区湿地土壤全氮和全磷含量的估算获得了较高精度;对盘锦湿地土壤全氮含量的估算,最高估算精度产生于CR光谱变换技术结合bootstrap PLSR建模;对湿地土壤全磷含量的估算,最高估算精度产生于原光谱数据结合bootstrap PLSR建模;模拟高光谱数据Hyperion对湿地土壤全氮和全磷含量的估算精度均高于模拟多光谱数据TM,模拟Hyperion的估算精度更接近于实测光谱的估算精度。     

乌兰布和沙漠东北缘荒漠-绿洲过渡带植被地上生物量估算

干旱区荒漠植被地上生物量是植被生长状况评价与荒漠化监测的重要指标。在乌兰布和沙漠东北缘的荒漠-绿洲过渡带选取典型区,基于地面调查数据构建主要植物种的异速生长方程,对样方内的植被地上生物量进行估算;基于样方调查数据和Quick Bird影像数据,分别建立植被指数与人工固沙林和荒漠植被地上生物量的回归模型,并对研究区植被地上生物量进行估算。结果表明:植冠体积V是较好的预测变量,所得荒漠植物异速生长方程精度较高,能够满足样方内荒漠植被地上生物量估算需要;采用RVI对数模型估算人工固沙林地上生物量的效果最好(R~2=0.72,RMSEP=56.15),采用RVI线性模型估算荒漠植被地上生物量的效果最好(R~2=0.82,RMSEP=15.07);研究区内荒漠植被和人工固沙林的单位面积地上生物量分别为90.73g/m~2和105.28g/m~2。该研究可以为荒漠化监测和荒漠植被遥感信息提取提供参考。     

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images.The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models.This new function is a combination of the gray-level information and first-order statistical features,called standard deviation parameters.In a comprehensive study,the developed algorithm and the efficiency of segmentation were first tested for synthetic images.Tests were also performed on breast and liver ultrasound images.The proposed method was compared with the watershed approach to show its efficiency.The performance of the segmentation was estimated using the area error rate.Using the standard deviation textural feature and a 5×5 kernel,our curve evolution was able to produce results close to the minimal area error rate(namely 8.88% for breast images and 10.82% for liver images).The image resolution was evaluated using the contrast-to-gradient method.The experiments showed promising segmentation results.     

一种青海湖流域消除植被光谱对土壤光谱影响的土壤有机质含量遥感估算方法

基于遥感技术手段快速测定区域尺度土壤有机质含量(SOM), 对气候、陆地生态系统和农业等领域具有重要的作用和意义。但现有的多光谱遥感影像因其波段宽度较窄, 包含的土壤有机质信息有限, 导致其估算结果的可靠性与精度较低。为此, 以青海湖流域为实证试验区, 将2016 年9 月底(此时, 青海湖流域牧草等植被停止生长, 土壤有机质积累达到全年最高)地面采集并测定的土壤有机质含量数据与同时期MODIS 黑空BRDF/Albedo 产品的宽、窄波段进行了对比与检验。发现: BRDF/Albedo 宽波段的相关性(近红外、短波波段相关系数分别为0.704 和 0.670)高于窄波段相关性(第2, 5, 6 波段的相关系数分别是0.583、0.631 和0.625), 证实了宽波段含有更加丰富、完整的土壤有机质含量信息。为了进一步提高SOM 估算的精度, 基于梯形方法构建了宽波段近红外反照率/植被覆盖度梯形特征空间,从宽波段近红外反照率(包含植被、土壤混合光谱)中成功分离出裸土反照率, 并分别构建了SOM 遥感估算模型。经验证,消除了植被对土壤光谱影响的裸土反照率模型精度(均方根误差为16.87、平均绝对百分比误差为30.0%, 希尔不等系数为0.22)高于宽波段近红外反照率模型精度(均方根误差为20.12、平均绝对百分比误差为31.0%, 希尔不等系数为0.27)。该方法简单易操作, 不仅有助于提高表层土壤有机质含量遥感估算的精度, 也可为土壤其他属性如N, P等元素含量的遥感估算提供了新思路。     

四川柏木人工林林下植被生物量与林分结构的关系

森林结构与林下植被生物量的关系是森林持续经营与森林碳计量监测的科学基础,但一直缺乏必要的研究。以四川柏木(Cupressus funebris)人工林为研究对象,揭示林下植被生物量(Wu)、灌木生物量(Ws)和草本生物量(Wh)与林分结构的关系,并试图构建区域性林下植被生物量估测的混合模型。结果表明:(1)乔、灌、草群体共12个结构因子中,灌木群体的平均基径(Ds)、盖度(Cs)、高度(Hs)、体积(Vs)与林下植被生物量关系更紧密,在林下植被生物量模型构建中更有效;(2)多模型拟合与比较表明,柏木林Ws最佳估算模型为Ws=0.0005V1.0411s(R2a=0.762,P0.001,n=40),而Wu的最佳估算模型为ln Wu=0.0158Hs+0.0111Cs-0.5358(R2a=0.695,P0.001,n=40),但对于Wh未能获得较为理想的估算模型(R2a0.410,P0.01,n=40);(3)林分密度(Du)整合进入多元线性模型提高了林下植被生物量的估测精度,ln Wu=a+b Du+c Hs+d Cs(R2a=0.721,P0.001,n=40)。研究为区域性林下生物量估测模型构建提供了新论据。     

基于减量精细采样法估算小麦叶片氮积累量的最佳归一化光谱指数

基于6个小麦品种、5个施氮水平、4年田间试验条件下不同生育时期的小麦叶片高光谱反射率和相应的氮含量及生物量,采用减量精细采样法,系统构建了350～2500 nm范围内所有两两波段组成的归一化光谱指数［NDSI（i, j）］,综合分析了小麦叶片氮积累量(LNA, g N·m^-2)与NDSI（i, j）的定量关系,确定了估算叶片氮积累量的新高光谱特征波段和光谱指数,进而建立了小麦叶片氮积累量监测模型.结果表明：估算小麦叶片氮积累量的敏感波段主要存在于可见光区和近红外区,最佳特征波段组合为720 nm和860 nm;基于NDSI(860,720)的叶片氮积累量监测模型为LNA=26.34×［NDSI(860,720)］^1.887（R²=0.900,SE=1.327）.利用独立试验资料的检验结果表明,基于NDSI(860,720)建立的回归模型对小麦叶片氮积累量的估测精度为0.823,RMSE为0.991 g N·m^-2,模型预测值与观察值之间的符合度较高.可利用新的归一化高光谱参数NDSI(860,720)来估算小麦叶片氮积累量.     

基于ASD和GaiaSky-mini的农田土壤重金属污染监测

农田土壤重金属污染已经成为现代社会不容小视的环境问题,传统化学检测方法相对落后,进行动态、迅速、大范围的土壤重金属监测迫在眉睫。分别利用便携式地物光谱仪(ASD)和机载高光谱成像系统(Gaia Sky-mini)获取的光谱数据进行土壤重金属污染监测。结果表明,在As、Cd、Cr、Pb 4种重金属元素的偏最小二乘法(PLSR)预测模型中,基于Gaia Sky-mini光谱可以区别As的高低值,同时,还具备粗略估算样本Cd含量的能力,相对分析误差(RPD)值分别为1.13和1.50;ASD光谱数据可以粗略估算As的含量,定量估算Cd的含量,As和Cd元素RPD值最高分别为1.45和1.95。对比两种数据源回归结果,低空无人机获取的高光谱数据可以监测土壤重金属,光谱波段更宽、光谱分辨率更高的ASD数据对土壤重金属监测精度更高。低空无人机高光谱数据的应用对进一步研究快速、大尺度监测土壤重金属含量提供了更多的手段,具有重要意义。     

Optimization and performance validation for the fully automatic blood type analysis system

To evaluate and validate the application of fully automatic blood type analysis system parameters under actual lab conditions. All key system parameters were optimized and validated accordingly. The optimized parameters were centrifugal speed at 550 rpm; centrifugal time 20 min; resuspension speed 1,200 rpm; resuspension time: 45 s; incubation temperature 25℃ ; incubation time 400 s; incubation rate: 0 rpm. The sampled red blood cell concentration was 3%. The ratio of plasma to red blood cells reagent was 60 μL:30 μL; the ratio of antibody (reagent) to sampled diluted red blood cell was 30 μL:30 μL. After applying our key parameters for optimization and validation, the automatic blood type detection system''s performance was found to meet the relevant requirements, effectively improving the accuracy and reliability of the detection system.     

基于Hyperion影像植被光谱的土壤重金属含量空间分布反演——以青海省玉树县为例

本研究主要探讨了利用Hyperion影像植被光谱估算土壤重金属含量的可行性.以野外采集的三江源区玉树县48个表层土壤样品As、Pb、Zn、Cd实验室测定含量值,以及从两景Hyperion影像提取的48个土壤样本点相应的176个植被光谱反射率波段及构建的5种植被指数为数据源,利用偏最小二乘回归方法(PLSR)建立土壤各重金属含量与上述两套Hyperion影像上提取的变量之间的估算模型.模型分别为176个植被光谱反射率波段与土壤各重金属含量间的估算模型(植被光谱反射率模型),和以5种植被指数作为自变量,与土壤各重金属含量建立的估算模型(综合植被指数模型).运用验证样本的4种重金属元素实测含量值的标准差与均方根误差的比值(RPD)作为检验标准,As、Pb两种模型RPD均小于1.4,不具备粗略估算能力;Zn、Cd两种模型RPD分别为1.53、1.46与1.46、1.42,均具备粗略估算能力.根据上述结果将Zn的光谱反射率估算模型与Hyperion影像相结合反演得到土壤重金属Zn含量的空间分布,Zn含量在214国道、308省道和乡镇附近偏高,主要受到较强的人类活动影响.表明运用Hyperion高光谱影像植被光谱反射率可以间接估算土壤Zn、Cd元素含量.     

A modified statistically optimal null filter method for recognizing protein-coding regions

Computer-aided protein-coding gene prediction in uncharacterized genomic DNA sequences is one of the most important issues of biological signal processing.A modified filter method based on a statistically optimal null filter(SONF) theory is proposed for recognizing protein-coding regions.The square deviation gain(SDG) between the input and output of the model is used to identify the coding regions.The effective SDG amplification model with Class I and Class II enhancement is designed to suppress the non-coding regions.Also,an evaluation algorithm has been used to compare the modified model with most gene prediction methods currently available in terms of sensitivity,specificity and precision.The performance for identification of protein-coding regions has been evaluated at the nucleotide level using benchmark datasets and 91.4%,96%,93.7% were obtained for sensitivity,specificity and precision,respectively.These results suggest that the proposed model is potentially useful in gene finding field,which can help recognize protein-coding regions with higher precision and speed than present algorithms.     

光周期诱导红腹锦鸡冬季繁殖效果初报

?College of Life Science, Hebei Normal University, Shijiazhuang 050016, China)Golden pheasant ( Chrysolophus pictus) is a monotypic and special species of China.In nature, it breeds once a year in spring summer.From Oct.1998 to Feb.1999, we studied the effects of photo periods on inducing the birds to reproduce in winter and succeeded.In the experiment, 36 adult females and 18 males were divided into three groups.Each group was divided into 6 replicates, each replicate included 2 females and 1 male, and fed in one cage.All the groups were exposed to short day (8L∶16D) for 8 weeks, and then transferred to long day (12L∶12D, 14L∶10D or 16L∶8D). In result, photoperiods significantly affected Golden pheasant reproductive performance in winter.Considered of all of the reproductive performance indicators, the reproductive performance of the group exposure to 14L∶10D was the best, the 16L∶8D group's was less and the 12L∶12D group's was the worst.It may be that the birds' CDL (critical day length) for inducing egg production was about 12 h, and the CDL for optimal egg production was 14L∶10D in winter. The birds exposed to 12L∶12D had no fertilized egg.The other groups had fertilized eggs and the fertility was 15 05% (14L∶10D) and 20 41% (16L∶8D).The hatchability of fertilized egg were 76 47% (14L∶10D) and 80 00% (16L∶8D). The egg laying pattern was affected by the size of photoperiod.The ratio of lay of the birds exposed to 14L∶10D, increased rapidly and sustained in a peak level for 5 weeks, and then fall down.But the other birds (group A and C) increased to the peak at the first week and then fall down quickly.     

遥感技术支持下的植被生产力与生物量研究进展

目前广泛应用于植被生产力与生物量估算的遥感模型主要有经验模型、物理模型、半经验模型和综合模型 ,它们的应用受到诸如大气、背景、地形、植被覆盖率与结构等因素的影响。遥感技术的迅速发展及其它技术的应用 ,包括热红外、微波和激光遥感仪器以及多角度、高光谱和高分辨率技术等 ,正逐步消除或降低影响因素 ,进一步提高植被生产力与生物量估算的范围和精度     

基于红边参数的芦苇叶绿素的反演研究

干旱区荒漠植物的叶绿素定量反演是动态监测、快速有效评估植物生物量及长势的有效方法。利用便携式可见-近红外光谱仪FieldSpecPro3测定绿洲、盐碱地及沙漠3种生境内的芦苇高光谱值, 对高光谱数据一阶微分以及红边参数与叶绿素含量进行了相关分析, 选取最佳红边参数建立经验估算模型与神经网络模型, 并评估检验。模型显示, 三种生境下均为二项式回归模型的决定系数最佳, 检验精度的决定系数(R²)分别为0.8466、0.8672和0.7935, 均方根误差 RMSE (root-mean-square error)分别为2.3601、1.4112和2.8002; BP神经网络模型的检验精度的决定系数(R²)分别为0.9147、0.9331和0.8813, RSME分别为1.4010、0.9964和0.5559。结果表明, 利用BP(back propagation)神经网络估算的模型精确度显著提高, 可作为芦苇叶绿素高光谱反演的有效模型而使用, 为荒漠植物叶片叶绿素的光谱特征反演提供了借鉴, 为监测荒漠植物生长、产量估算及动态监测等提供可行的手段。     

中国陆地植被净初级生产力估算模型优化与分析——基于中国生态系统研究网络数据

该研究基于中国生态系统研究网络(CERN)数据对传统CASA模型进行优化,对比两叶模型与优化CASA模型在站点尺度和像元尺度对于8个典型生态站点的植被净初级生产力(NPP)估算精度,选择在像元尺度表现更好的优化CASA模型,结合中国土地覆被数据(ChinaCover)开展2000—2019年中国陆地植被NPP监测与分析。研究结果表明：(1)基于FY2D PAR的优化方案能够有效避免空间插值导致的不确定性问题,显著提高了PAR估算精度;(2)在站点尺度上,两叶模型用于估算典型森林、草地生态系统的NPP表现更好,而在像元尺度上优化CASA模型估算精度更高;(3)在全国尺度上,优化了最大光能利用率、水分胁迫系数以及光合有效辐射计算方法的CASA模型能够较好地模拟中国陆地植被NPP,近20年中国陆地植被NPP变化范围为2.703—2.882 PgC/a,在空间上呈西北低东南高的格局,在时间上呈现波动中缓慢增加的趋势。     

基于机载激光雷达与Landsat 8 OLI数据的亚热带森林生物量估算

快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(Li DAR)点云与Landsat 8 OLI多光谱数据,借助江苏省常熟市虞山地区55块调查样地数据,首先提取并分析了87个特征变量(53个OLI特征变量,34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选,然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型),并与基于两种数据建立的综合生物量估算模型的结果进行比较,讨论预测结果及其精确性。结果表明:3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时,地上和地下生物量的估算精度均达到0.4以上,基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高,达到0.67及以上。利用分森林类型模型估算生物量,综合生物量估算模型精度(地上生物量:R2为0.88;地下生物量:R2为0.92)优于OLI生物量估算模型(地上生物量:R2为0.73;地下生物量:R2为0.81)和Li DAR生物量估算模型(地上生物量:R2为0.86;地下生物量:R2为0.83)。     

高原鼠兔种群的性比

The sex ratio (♂ / ♀ ) of plateau pika's (Ochotona curzoniae) population was studied by re-captured method in the region of the Haibei Mpine Meadow Ecosystem Research Station, Northwest Institute of Plateau Biology, the Chinese Academy of Sciences from April of 2001 to August of 2002. The result showed that there was no significant difference from 1:1 in adult's sex ratio in whole breeding season, whereas the sex ratio of juvenile had some fluctuations among different age stages. The sex ratio of the second litter varied significantly from embryo to 60-day-old, but no difference at the first and the third litter. We concluded it was caused by conflict between maternal strategy and juvenile's strategy. No significant differences of sex ratio were found both in adult and juvenile between 2001 and 2002. The sex ratio of plateau pika before and after overwintering did not vary. In summary,we proposed that sex ratio of plateau pika's population was not influenced by exogenous factors, but some serf-regulation mechanisms may be involved.