Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.     

The Negative Binomial Distribution of Order k and Some of Its Properties

The negative binomial distribution of order k is introduced and briefly studied. First it is shown that it is a proper probability distribution. Then its probability generating function, mean and variance are derived. Finally it is shown that the number of trials until the rth kth consecutive success (r ≧ 1, k ≧ 1) in independent trials with constant success probability p (0 < p < 1) is distributed as negative binomial distribution of order k. The present paper generalizes results of SHANE (1973), PHILIPPOU and MUWAFI (1982), and PHILIPPOU, GEORGHIOU and PHILIPPOU (1982).     

辽东山区天然次生林的数量分类

结合DCA排序和TW IN SPAN分类结果,将辽东山区天然次生林划分为5个群落类型:花曲柳林、蒙古栎林、阔叶混交林、水曲柳林、胡桃楸林。DCA排序与TW IN SPAN分类产生了较一致的分类结果。DCA第一轴代表的环境意义为坡向。花曲柳林多分布于阳坡的中坡及中上坡,蒙古栎林多分布在中上坡。花曲柳与蒙古栎常混生在一起,随着坡位上升,花曲柳优势度下降,而蒙古栎优势度增加。阔叶混交林多位于阴坡,乔木层没有稳定和绝对优势种,多以假色槭、风桦、色木槭为优势种,但假色槭分布于乔木层第2亚层。水曲柳林多分布在中下坡。胡桃楸分布在山下部,喜潮湿生境。色木槭在辽东山区分布广泛,重要值较高,且更新良好,很可能是群落演替顶级物种。辽东山区天然次生林林下藤本植株数量多,以五味子、软枣猕猴桃、狗枣猕猴桃为主。     

森林粗死木质残体的概念及其分类

森林粗死木残体 (Coarse woody debris,CWD)在不同的文献中有不同的定义 ,没有通用而确切的概念用来描述 CWD,对研究结果的比较造成了很大障碍。 2 0世纪 90年代以来 ,随着景观生态学的发展 ,以及对 CWD生态功能的深入研究 ,国外的森林管理和研究机构 (例如 USDA Forest Service和 L TER)为了把 CWD放在区域以及景观尺度上进行比较 ,对 CWD的概念等进行了统一 ,将其直径标准由原来的≥ 2 .5 cm调整到≥ 10 cm,但是我国在此方面还没有与国际接轨 ,仍采用旧标准 (≥ 2 .5 cm ) ,这样的研究结果难于和国外进行比较 ,不利于我国 CWD的长期深入研究。另外 ,有关 CWD的分类一直以来也没有形成一个完整的分类系统 ,我国也缺少 CWD分类方法的介绍。鉴于以上情况 ,综合国内外近年来在 CWD方面的研究动态 ,综述了 CWD的概念和分类情况 ,并初步提出较综合的 CWD概念及其分类系统 ,以供相关研究者讨论和参考 ,为我国的 CWD研究起到推动作用     

一株与鳆发光杆菌最相似细菌的研究

对从海鲜食品中分离到的1株少见细菌M1进行系统分类鉴定。采用常规方法[1]进行分离培养,以形态学特征、培养特性、生理生化特征以及分子生物学等方法对其进行分类鉴定。结果可见,该菌株为革兰氏阴性杆状细菌,16SrDNA核甘酸序列测定与鳆发光杆菌最为相似,生理生化特征与发光杆菌属、弧菌属相近,但都不完全相符,而与鳆发光杆菌相似性最高。     

桃花水母

介绍了桃花水母的生活习性、形态、生理、分类、分布,讨论了桃花水母的研究意义及经济价值。     

A revised classification of the higher taxa of the Ostracoda (Crustacea)

Focusing on palaeontological data from the literature of the last four decades, a new classification of the Ostracoda is outlined. The superorder Podocopomorpha is accepted as a mantle term for the ‘P-orders’ Palaeocopida, Punciocopida, Platycopida and Podocopida; its counterpart are the Myodocopomorpha (Cypridinida, Halocypridida and ? Leperditicopida). The Podocopida comprise the suborders Bairdiocopina, Cytherocopina, Sigilliocopina, Cypridocopina, Darwinulocopina and Healdiocopina (n.;=Metacopina s.s.). The non-calcifying ostracodomorph arthropods (Archaeocopida and Phosphatocopida) of the Early Palaeozoic, now excluded from the Ostracoda by many authors, are also considered briefly.     

我国植被数量分析方法的研究概况和发展趋势

植被数量分析是现代植被研究的重要手段,数量分类和排序是现代植被生态学研究最重要的,也是应用最广泛的生态学技术。数量分析方法在20世纪50年代引入植被生态学研究领域,我国学者在70年代后期开始研究植被的数量分类和排序。本文主要从相关资料、应用研究、新方法研究三个方面论述了我国植被数量分析方法的发展,重点阐述了从20世纪70年代后期以来出现的并且被广泛应用的新方法及其应用研究概况,并在此基础上分析了植被数量分析方法未来的发展趋势。     

Limits to the estimation of species richness: The use of relative abundance distributions

The present study demonstrates the possibility of estimating species numbers of animal or plant communities from samples using relative abundance distributions. We use log‐abundance–species‐rank order plots and derive two new estimators that are based on log‐series and lognormal distributions. At small to moderate sample sizes these estimators appear to be more precise than previous parametric and nonparametric estimators. We test our estimators using samples from 171 published medium‐sized to large animal and plant communities taken from the literature. By this we show that our new estimators define also limits of precision.     

The use of indicator taxa as representatives of communities in bioassessment

1. Sampling and processing of benthic macroinvertebrate samples is time consuming and expensive. Although a number of cost‐cutting options exist, a frequently asked question is how representative a subset of data is of the whole community, in particular in areas where habitat diversity is high (like Dutch surface water habitats). 2. Weighted averaging was used to reassign 650 samples to a typology of 40 community types, testing the representativeness of different subsets of data: (i) four different types of data (presence/absence, raw, ²log‐ and ln‐transformed abundance), (ii) three subsets of ‘indicator’ taxa (taxa with indicator weights 4–12, 7–12, and 10–12) and (iii) single taxonomic groups (n = 14) by determining the classification error. 3. ²log‐ and ln‐transformed abundances resulted in the lowest classification error, whilst the use of qualitative data resulted in a reduction of 10% of the samples assigned to their original community type compared to the use of ln‐transformed abundance data. 4. Samples from community types with a high number of unique indicator taxa had the lowest classification error, and classification error increased as similarity among community types increased. Using a subset of indicator taxa resulted in a maximum increase of the classification error of 15% when only taxa with an indicator weight 10–12 were included (error = 49.1%). 5. Use of single taxonomic groups resulted in high classification error, the lowest classification error was found using Trichoptera (68%), and was related to the frequency of the taxonomic group among samples and the indicator weights of the taxa. 6. Our findings that the use of qualitative data, subsets of indicator taxa or single taxonomic groups resulted in high classification error implies low taxonomic redundancy, and supports the use of all taxa in characterising a macroinvertebrate community, in particular in areas where habitat diversity is high.