First insights on spatial and temporal distribution patterns of humpback whales in the breeding ground at Sainte Marie Channel,Madagascar

The Sainte Marie Channel on the northeast coast of Madagascar is an important breeding ground for humpback whales; the first observation of birth was documented there, yet it has never been investigated for phenology and habitat use of humpback whales. The present study was aimed at examining temporal and spatial distribution patterns and the encounter rate of different social groups of humpback whales during the breeding season. We used a large set of opportunistic sightings data collected from whale-watching boats. A total of 3 247 sightings were collected during 897 whale-watching trips conducted between June and September from 2009 to 2013. Our study complements previous information on the seasonal presence of humpback whale social groups by demonstrating a persistent and well-structured temporal pattern in the succession of the different groups. Over the different years of the study period, groups without calves consistently dominated the first 30 days of the breeding season, followed by an increase in groups with calves. Interannual differences were observed in the encounter rates, with significantly higher global encounter rates in 2009, 2011 and 2013 (2.2, 2.3 and 2.3 sightings h^–1, respectively), and likewise for the mean encounter rates for groups with calves. In contrast, the encounter rate of groups without calves was similar over the study period. Although our study area exhibits a narrow configuration and poorly contrasting physiographic features due to its restricted spatial extent, we report a spatially segregated pattern of humpback whale social groups in the Sainte Marie Channel. A general linear model showed that groups with calves were influenced by water depth and distance from shore, being observed mostly in shallow waters (to 20 m depth) and close to the coastline (6 km). Our findings will be useful for the development of strategic sustained management plans by providing baseline information on humpback whale distribution at an important but poorly documented breeding site.     

基于决策树的漓江上游土地覆盖分类

针对山区植被分类受地形复杂、植被类型多样、验证数据获取困难等因素限制基于多光谱数据的亚热带山区土地利用/覆盖分类存在困难,探究利用物候信息对亚热带山区植被实施分类的效果。综合运用归一化植被指数(NDVI)、比值植被指数(RVI)、归一化水指数(NDWI),同时考虑到海拔高度对植被类型的影响,建立决策树模型。该模型基于多时相Landsat TM影像,利用了不同地物类型的物候特征和光谱差异,将漓江上游地区分为8种土地覆盖类型。实验结果表明,分类结果总体精度达到86.40%,Kappa系数为0.83。     

The distribution of rare phenolic type compounds in wild and cultivated wheats

In 14 species of Aegilops, four species of Triticum and two species of Hordeum, a complex of rare phenolic compounds was found and showed quantitative differences between the wild and domestic species. There was a dramatic reduction in the cultivars to about 5% of the amount of these phenolic constituents. In the wild species these phenolic compounds may act as important germination regulators, such as water-dissolving germination inhibitors which may act as 'rain gauges'. This is especially important for the germination at the appropriate time in species inhabiting arid regions or deserts.     

太平天国战争前后安徽的垦殖考量

以安徽垦殖为例,充分考虑各种误差因素,把误差控制在一定范围的要求,借助历史学的研究方法对文献资料进行考证与鉴别,运用现代的统计科学对数据进行一致性检验与修正,尝试对太平天国战争前后安徽的垦殖变动进行量化处理,提出耕地数据重建及无资料时段垦殖率插补的可行性方法,并以此完成战争前后分县垦殖率的重建,进而利用GIS再现区域垦殖变化格局。资料检验表明,这是一种较为合理的土地利用空间格局度量方法。     

早熟苹果良种——珊夏引种及早期丰产栽培

珊夏适应性强,耐瘠薄,早花早果,座果率高,早期丰产,果大,品质优,是早熟苹果优良品种。本文对珊夏苹果的物侯期、生长结果习性、果实经济性状、适应性及其栽培要点作了简单介绍。     

Research on Forest Phenology Prediction based on LSTM and GRU Model

Research on forest phenology is an important parameter related to climate and environmental changes. An optical camera was used as a near-earth remote sensing satellite device to obtain forest images, and the data of Green excess index (GEI) in the images were calculated, which was fitted with the seasonal variation curve of GEI data by double Logistic method and normalization method. LSTM and GRU deep learning models were introduced to train and test the GEI data. Moreover, the rationality and performance evaluation of the deep learning model were verified, and finally the model predicted the trend of GEI data in the next 60 days. Results showed: In the aspects of forest phenology training and prediction, GRU and LSTM models were verified by histograms and autocorrelation graphs, indicating that the distribution of predicted data was consistent with the trend of real data, LSTM and GRU model data were feasible and the model was stable. The differences of MSE, RMSE, MAE and MAPE between LSTM model and GRU model were 0.0014, 0.013, 0.008 and 5.26%, respectively. GRU had higher performance than LSTM. The prediction of LSTM and GRU models about GEI data for the next 60 days both showed a trend chart consistent with the change trend of GEI data in the first half of the year. GRU and LSTM were used to predict GEI data by deep learning model, and the response of LSTM and GRU deep learning models in forest phenology prediction was realized, and the performance of GRU was better than that of LSTM model. It could further reveal the growth and climate change of forest phenology in the future, and provide a theoretical basis for the application of forest phenology prediction.     

大气CO_2浓度升高对土壤中不同粒级碳的影响

不同粒级土壤中的碳有着不同的周转规律,在高CO2浓度条件下,它们含量的变化将在一定程度上反映土壤碳是累积还是减少,对明确土壤碳的变化趋势有重要意义.采用田间培养试验初步模拟研究在高CO2浓度条件下土壤不同粒级碳的分布.结果表明,加入秸秆培养1年,由于CO2浓度升高的原因导致在低氮(LN)、常规氮(NN)和高氮(HN)水平下土壤中碳分别增加0·01、1·10、1·22g/kg,表现为粒级<53μm土壤颗粒中碳分别增加1·53、2·19、2·70g/kg.粒级<53μm土壤颗粒碳量的增加,主要是由于其重量分配百分数显著增加36·2%,碳浓度增加5·4%;粒级>250μm和250~53μm土壤颗粒部分虽然其碳浓度分别增加20·8%和17·3%(P<0·05),但由于重量分配百分数分别显著降低22·8%和36·1%,结果碳量降低.试验表明高CO2浓度导致不同粒级土壤的分配及碳浓度的变化;高氮施肥水平下有增加土壤碳量特别是小粒级土壤碳量的趋势.     

再论中国稻作的起源发展和传播

根据较新的考古资料 ,认为目前所知世界上最早的稻谷遗存是在湖南道县和江西万年 ,最早的栽培稻实物是在湖南澧县 ,因此 ,长江中游地区是中国稻作的起源地。 12 0 0 0aBP以来 ,史前稻作的发展经历了起源、确立、扩展、兴盛 4个阶段。稻作的传播 ,先是从起源地向东扩展 ,尔后向北和向西扩展。约 35 0 0aBP之后 ,稻作从长江下游地区传到日本 ,再扩展到朝鲜半岛。越南和泰国的稻作最初可能不是从中国传去 ,而是源自印度     

豫北沙地果树适宜树种及高产优质高效开发技术研究

通过品种引进、试验和鉴评,筛选出适宜于豫北沙地栽培的优良果树品种28个;栽培实践表明,高接换优技术和控冠促果措施以及生长调节剂的结合使用,是提高果树成花率、坐果率及产量的有效途径。通过丰产园栽培试验,提出矮化无病毒苹果丰产栽培,和梨"矮、密、早、丰"的配套栽培技术,及和土肥管理与病虫害防治等一整套科学的管理措施。