Nest-cavity characteristics of the Great Spotted Woodpecker Dendrocopos major in shelter plantations of west Inner Mongolia

The Great Spotted Woodpecker Dendrocopos major （L.）, one of the natural predators of Anoplophora glabripennis （Motsch.） （Coleoptera： Cerambycidae）, is resident to Wulate Qianqi County of the Inner Mongolia and widely found in shelter plantations. In August 2005 and 2006, 174 and 153 nest-cavities of Great Spotted Woodpeckers were found respectively in Wulate Qianqi County and 22 breeding nest-cavities were investigated in 2007. The results showed that mostly willow species were selected for nesting by the Great Spotted Woodpecker, but mature poplar trees also could be chosen. Nest cavities were often found with a protuberance above the cavity entrance or with a downward sloping gradient, or both. The selection of the height of the nest-cavity height was not significant. The vertical diameter of the nest-cavity entrance （VDE） and the horizontal diameter of the nest-cavity entrance （HDE） ranged from 5.0 to 5.8 cm. The results also indicated that the compass orientation of more than 60% of nest-cavities were towards the north, northeast and east. This study suggests a convergence of some nest-cavity characteristics of the Great Spotted Woodpecker in shelter plantations and will help us to make artificial nest for conserving the woodpecker and, as well, use the bird for controlling pests.

Stock Selection Based on a Hybrid Quantitative Method

Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.

Comparison of algorithms for monitoring wheat powdery mildew using multi-angular remote sensing data

Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.

Telemetric Data Reveals Ecolgoically Adaptive Behavior of Captive Raised Chinese Giant Salamanders When Reintroduced into Their Native Habitat

Little is known about the ecology of the Chinese Giant Salamander(Andrias davidianus), a critically endangered species. Such information is needed to make informed decisions concerning the conservation and management of this species. Four A. davidianus raised in a pool were released into their native habitat on 04 May 2005 and were subsequently radio-tracked for approximately 155–168 days. Following their release, the giant salamanders traveled upstream in search of suitable micro-habitats, and settled after 10 days. Later, a devastating summer flash flood destroyed the salamanders' dens, triggering another bout of habitat searching by the animals. Eventually, the salamanders settled in different sections of the stream where they remained until the end of the study. On average, each habitat searching endeavor took 7.5 days, during which a giant salamander explored a 310 m stretch of stream with a surface area of about 1157 m2 and occupied 3.5 temporary dwellings. Each giant salamander spent an average of 144.5 days in semi-permanent micro-habitats, and occupied territories that had a mean size of 34.75 m2. Our results indicate that the Chinese giant salamander responds to habitat disturbance by seeking new habitats upstream, both water temperature and water level affect the salamander's habitat searching activity, and the size of the salamander's semi-permanent territory is influenced by the size of the pool containing the animal's den.

Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content

Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems.The application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture sensor.In this study,a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured.Isolation Forest algorithm(iForest)was used to eliminate outliers from the data.Based on the root mean square error of prediction RMSEP of Back Propagation Neural Network(BPNN)model results,a series of new swarm intelligence algorithms,including Manta Ray Foraging Optimization(MRFO),Slime Mould Algorithm(SMA),etc.,were used to select the characteristic wavelengths of soil moisture.The analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model prediction.By comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm(GA)and Particle Swarm Optimization(PSO),it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil moisture.Integrating the results of all intelligence algorithms used,soil moisture sensitive wavelengths were selected as 490 nm,513 nm,543 nm,900 nm and 926 nm,which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.