Ecological relevance of associative learning in fruit fly larvae

A few invertebrate models have been used for studying neurobiological and molecular aspects of associative learning. The ecological and evolutionary aspects of associative learning in these invertebrates are not yet well understood. To further this knowledge, I tested fruit fly larvae for their ability to learn to associate odors with preferred environmental conditions. The larvae learned to avoid odors associated with predation and to prefer odors associated with high-quality food, but failed to learn about odors associated with optimal temperature. It appears that the larvae possess a general ability to evaluate a suite of environmental parameters and associate preferred conditions with relevant stimuli. Received: 6 May 1998 / Accepted after revision: 11 October 1998     

土地退化研究综述

在归纳总结国内外学者对土地退化的概念内涵界定的基础上，综述了土地退化机理、类型划分、评价与监测、及防治与生态重建等方面的国内外研究进展，并在此基础上提出了进一步深入研究我国土地退化问题的方向：1)土地退化评价指标体系的研究；2)土地退化监测与预警的系统研究；3)重点土地退化类型及其退化机制研究；4)退化土地生态重建的原理、方法及技术体系；5)用经济手段进行生态环境建设；6)构建退化土地生态重建的政策保障体系；7)重建技术与模式的试验示范。     

钾离子在土壤中吸附和解吸动力学研究进展

就国内外有关钾离子在土壤中的吸附、解吸动力学的研究进行了简要的综述。主要论述的内容包括土壤钾离子吸附、解吸动力学常用数学模型。土壤吸附、解吸钾离子的机理，以及影响土壤对钾离子吸附、解吸的因素。最后提出了有待进一步研究的问题。     

草甘膦对非靶标生物的毒性研究进展

随着现代农业的飞速发展,农药的使用越来越广泛。草甘膦作为全球销量最大的农药除草剂,在自然环境中广泛存在,其对生态环境和人类健康的影响越来越受到人们的关注。文中从水生/两栖动物、土壤生物、哺乳动物以及人群等4个方面对除草剂草甘膦对非靶标生物的毒性研究概况进行综述,并对该领域未来发展趋势做了简要分析与展望,以期为草甘膦的生态安全和环境风险评价提供数据资料,为其合理使用提供理论指导。     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

One Hundred Questions of Importance to the Conservation of Global Biological Diversity

Abstract:  We identified 100 scientific questions that, if answered, would have the greatest impact on conservation practice and policy. Representatives from 21 international organizations, regional sections and working groups of the Society for Conservation Biology, and 12 academics, from all continents except Antarctica, compiled 2291 questions of relevance to conservation of biological diversity worldwide. The questions were gathered from 761 individuals through workshops, email requests, and discussions. Voting by email to short-list questions, followed by a 2-day workshop, was used to derive the final list of 100 questions. Most of the final questions were derived through a process of modification and combination as the workshop progressed. The questions are divided into 12 sections: ecosystem functions and services, climate change, technological change, protected areas, ecosystem management and restoration, terrestrial ecosystems, marine ecosystems, freshwater ecosystems, species management, organizational systems and processes, societal context and change, and impacts of conservation interventions. We anticipate that these questions will help identify new directions for researchers and assist funders in directing funds.     

Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning

A model to mimic the search behaviour of fishermen is built with two neural networks to cope with two separate decision-making processes in fishing activities. One neural network deals with decisions to stay or move to new fishing grounds and the other is constructed for the purpose of finding prey within the fishing areas. Some similarities with the behaviour of real fishermen are found: concentrated local search once a prey has been located to increase the probability of remaining near a prey patch and the straightforward movement to other fishing grounds. The artificial fisherman prefers areas near the port when conditions in different fishing grounds are similar or when there is high uncertainty in its world. In the latter case a reluctance to navigate to other areas is observed. The artificial fisherman selects areas with higher concentration of prey, even if they are far from the port of departure, unless a high uncertainty is related to the fishing ground. Connected areas are preferred and followed in orderly fashion if a higher catch is expected. The observed behaviour of the artificial fisherman in uncertain scenarios can be described as a risk-averse attitude. The approach seems appropriate for an individual-based modelling of fishery systems, focusing on the learning and adaptive characteristics of fishermen and on interactions that take place at a fine scale.     

Eigenbehaviors: identifying structure in routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James). An erratum to this article can be found at     

Sympatric species of threespine stickleback differ in their performance in a spatial learning task

Increasing evidence suggests that cognitive function is under selection in diverse taxa and that this results in different cognitive abilities in taxa experiencing different selective environments. For example, species inhabiting spatially complex environments might be expected to have good spatial learning ability. We investigated whether local habitat conditions influence learning by comparing the performance of two recently diverged species of threespine stickleback (Gasterosteus aculeatus complex) in a spatial learning task. The two species reside sympatrically in several lakes. Benthics occupy the spatially structured vegetated littoral zone, whereas limnetics occupy the spatially homogenous open-water pelagic zone. We trained fish to locate a hidden reward in a T-maze and asked whether they did so by learning a body-centred pattern of movement (turn left or right) or by using plant landmarks. Both benthics and limnetics used turn and landmark cues, but benthics learnt the task almost twice as quickly as limnetics. This difference is consistent with the hypothesis that benthic and limnetic sticklebacks are equipped with spatial learning abilities well suited to the spatial complexity of their littoral and pelagic habitats. Our findings add to the understanding of the evolution of learning.     

Restrictions and options: A heuristic tool to integrate knowledge for strategies towards a sustainable development

SUMMARY

This article presents the heuristic ‘Restrictions and Options’ that was created in an interdisciplinary research team to identify restricting and supporting factors regarding sustainable ways of actions for different institutional and individual actors in the need-field of nutrition. The heuristic format is first presented on a general level. Then it is combined with two different approaches: first, with a systemic constructivist approach to organization theory, and second, with the ipsative theory of action (psychology). Its abilities are illustrated by examples from two research projects.

The heuristic tool was developed to help identify relevant factors that restrict or support sustainable development in two empirical settings and also could be used to compare the findings. Combining the two approaches remains for future research.     

环境生物学的研究现状与发展趋势

本文归纳和总结了环境生物学的含义及其研究内容和对象；探讨了环境生物学的形成与发展；确定了环境生物学的分类地位以及相关学科的关系；摸索了环境生物学的研究方法；提出了环境生物学的发展趋势。     

果树缺铁黄化研究进展

果树缺铁黄化是一个世界性的难题，各国对此进行了广泛深入的研究．本文根据国内外有关铁素营养方面研究的最新成果，对果树生长的主要障碍因子、植物对缺铁胁迫的反应和可能机理以及矫治措施等进行了综述．     

生态补偿理论研究现状与进展

全球生态环境问题已经引起世界各国的极大关注。生态系统及其服务的可持续性是人类社会可持续发展的决定性约束因素,人类赖以生存与发展的自然环境面临威胁,修复自然生态创伤,实施生态补偿是可持续发展的必然要求。一些生态补偿关键问题成为科学研究热点,如生态系统服务功能的价值核算、生态补偿的对象、标准、途径与方法,以及资源开发和重大工程活动的生态影响评价等。总结了国内外生态补偿相关研究成果,并进行综述,为开展生态补偿研究提供借鉴。     

环境中汞化学形态分析研究进展

作为一种剧毒元素,汞的毒性与其化学形态密切相关.环境介质中汞的化学形态监测分析主要涉及样品的采集、保存、前处理、分离和测定等步骤.本文综合国内外相关文献,系统介绍了开展环境介质中汞化学形态监测分析的相关测试技术和方法,并阐述了我国开展汞化学形态监测存在的问题及其意义.     

生物多样性保护及其研究进展（综述）

由于人口的增长和人类经济活动的加剧,致使生物多样性受到了严重的威胁,引起国际社会的普遍关注．生物多样性是生物及其与环境形成的生态复合体以及与此相关的各种生态过程的总和,具有十分重要的价值,是人类生存的物质基础．各国政府和有关的国际组织积极投入到保护生物多样性的全球行动中．为了促进保护工作,国内外都开展了相关的研究工作．综观该领域的研究现状,可以看出以下７个方面已成为当前生物多样性研究的热点：①生物多样性的调查、编目及信息系统的建立;②人类活动对生物多样性的影响;③生物多样性的生态系统功能;④生物多样性的长期动态监测;⑤物种濒危机制及保护对策的研究;⑥栽培植物与家养动物及其野生近缘的遗传多样性研究;⑦生物多样性保护技术与对策．结合我国的具体情况,建议优先考虑以下４个方面的研究：①生物多样性的调查、编目与动态监测;②物种濒危机制及保护对策的研究;③生物多样性的生态系统功能与生态系统管理;④栽培植物与家养动物及其野生近缘的遗传多样性研究．     

水田甲烷排放研究进展

本文对水田甲烷排放通量的研究进展及有关的土壤和环境因素作了综述。指出用于减少水田甲烷排放的措施不应导致水稻的减产和引起其它温室气体排放的增加。应开展落干期间水田作为大气甲烷汇的作用的研究。     

近5年增强UV-B辐射对植物影响的研究进展

综合分析了近5年增强UV-B辐射对植物影响的最新研究进展.结果发现近5年来增强UV-B辐射对植物影响的研究明显具有以下趋势:1)增强UV-B辐射对植物个体影响的研究总体上趋于减少;2)对植物信号转导的研究日益增多;3)植物对增强UV-B辐射胁迫的防卫机制和伤害修复研究仍是重点,但更偏重于防卫机制方面的研究;4)更加重视对植物群体及生态系统影响的研究;5)增强UV-B辐射与其它因子(环境背景因子和污染胁迫因子)的复合作用研究成为新的热点和重点.根据近5年的研究进展,推断在今后一段时间内,有关增强UV-B辐射对植物和生态系统影响的研究还会加强,信号转导、分子水平机理以及增强UV-B辐射与其它因子的复合作用研究可能是今后的研究热点.     

The dynamics of social learning in an insect model,the bumblebee (<Emphasis Type="Italic">Bombus terrestris</Emphasis>)

Bumblebees (Bombus terrestris) are attracted to those particular inflorescences where other bees are already foraging, a process known as local enhancement. Here, we use a quantitative analysis of learning in a foraging task to illustrate that this attraction can lead bees to learn more quickly which flower species are rewarding if they forage in the company of experienced conspecifics. This effect can also be elicited by model bees, rather than live demonstrators. We also show that local enhancement in bumblebees most likely reflects a general attraction to conspecifics that is not limited to a foraging context. Based on the widespread occurrence of both local enhancement and associative learning in the invertebrates, we suggest that social influences on learning in this group may be more common than the current literature would suggest and that invertebrates may provide a useful model for understanding how learning processes based on social information evolve.     

天然湿地生态系统评价技术研究进展

当前湿地科学已成为世界各国研究重点,其中湿地评价技术是热点之一。文章回顾了国内外河口、河流和湖库湿地生态系统评价指标体系、湿地生态环境质量现状及脆弱性及其生态系统健康评价技术、湿地生态系统功能、效益、价值评价技术研究现状以及新技术在上述这些领域中的应用。这些研究多是针对常规环境条件下小空间尺度、单一类型或单一动力作用下的湿地生态系统功能、效益和环境质量方面的评价。在此基础上,考虑宏观尺度上湿地生态系统与其周围生态系统的相互作用以及非常规环境条件下湿地生态系统的响应机制,提出完善天然湿地生态系统评价技术需要发展的几个方面,包括湿地-河流生态系统评价、湿地生态系统对临近区域及其子系统可持续发展的影响评价、湿地生态系统反馈评价以及极端条件下湿地生态系统评价技术。     

国内外可持续发展指标体系研究进展

可持续发展是人类社会发展的必由之路。可持续发展指标的筛选和指标体系的构建,是人类全面实施可持续发展战略的重要组成部分,是人类社会可持续发展研究中的前沿性课题。20世纪90年代以来可持续发展研究的热点已经从可持续发展的定义转向可持续发展的评价,特别是指标体系的构建。20多年来,可持续发展指标体系的研究已经出现了从理论探讨走向了实际应用的趋势。通过分析20世纪90年代以来国内外有代表性的不同尺度上的可持续发展指标体系：联合国可持续发展委员会（UNCSD）的可持续发展指标体系、世界银行衡量可持续发展的新指标体系、波罗的海21世纪议程、英国、瑞典、芬兰、德国、美国、瑞士、丹麦及中国等国的可持续发展指标体系、欧洲城市可持续发展指标体系、新西兰玛努卡市的可持续发展指标体系、美国西雅图市社区可持续发展指标体系、中国的云南省、海南省、山东省等省以及南京市和哈尔滨市的可持续发展指标体系、山西交口县可持续发展指标体系、云南山区民族行政村可持续发展指标体系,对国内外可持续发展指标体系研究进行了总体评价。结果发现如果在以西方文明为主的现代化和城市化的框架下进行区域可持续发展的分析和评判,那得到的结论本质上是区域现代化或城市化程度的反映,而不是对区域可持续发展现状和趋势的评判。20多年来,国内外可持续发展指标体系的研究取得了丰硕的成果,为人类实施可持续发展战略打下了坚实的基础。