Examining horizontal and vertical social ties to achieve social–ecological fit in an emerging marine reserve network

1. Most MPA networks are designed only with ecological processes in mind to increase their conservation utility. However, since MPA networks often involve large geographic areas, they also affect and involve multiple actors, institutions, and policy sectors.
2. A key challenge when establishing an effective MPA network is to align the ‘social system’ with the biophysical MPA network (the ‘ecological system’). This challenge is often denoted as ‘social–ecological fit’.
3. Facilitating collaborative social interactions among various actors and stakeholders (social connectivity) is equally as important as accomplishing ecological connectivity. New analytical approaches are required to effectively examine this ‘social’ dimension of fit.
4. An emerging marine reserve network in Jamaica and the recent invasion of Indo‐Pacific lionfish are used as a case study to: (1) examine the extent to which horizontal and vertical social ties bring local and national actors together to collaborate, coordinate, and share knowledge; and (2) assess the extent to which different attributes and features of such multilevel social networks may enhance or inhibit particular aspects of social–ecological fit.
5. Findings suggest that multilevel linkages have played the greatest role in relation to enhancing fit in the marine reserve network in the context of the recent lionfish invasion. However, the long‐term propensity of the multi‐actor and multilevel networks to enhance social–ecological fit is uncertain given the prevalence of weak social ties, lack of a culture of information sharing and collaboration, and limited financial resources.

     

浅谈如何做好档案管理工作

作者依据从事档案工作的实践,论述了做好档案管理工作的重要意义,并着重从相关部门领导应高度重视、档案管理部门应按规范严格执行档案管理工作流程和必须提高管理人员综合素质等方面进行阐述。可供从事档案管理工作者借鉴。     

Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil

Microbial communities vary across the landscape in forest soils, but prediction of their biomass and composition is a difficult challenge due to the large numbers of variables that influence their community structures. Here we examine the use of artificial neural network (ANN) models for extraction of patterns among soil chemical variables and microbial community structures in forest soils from three regions of the Atlantic Forest of Brazil. At each location, variations in soil chemical properties and FAME profiles of microbial community structures were mapped at 20 × 20 m intervals within 10 ha parcels. Geostatistical analyses showed that spatial variability in soil physical and chemical variables could be mapped at scale distances of 20 m, but that FAME profiles representing the microbial communities were highly variable and had no spatial dependence at the same scale in most cases. RDA analysis showed that FAME signatures representing different microbial groups were positively associated with soil pH, OM, P and base cations concentrations, whereas microbial biomass was negatively associated with the same environmental factors. In contrast, ANN models revealed clear relationships between microbial community structures at each parcel location, and generated verifiable predictions of variations in FAME profiles in relation to soil pH, texture, and the relative abundances of base cations. The results suggest that ANN modeling provides a useful approach for describing the relationships between microbial community structures and soil properties in tropical forest soils that were not able to be captured using geostatistical and RDA analyses.     

基于氨基酸分子中侧链基极性性质的DNA序列分类(英文)

从DNA序列片段个案中密码子分布密度角度出发,提取出DNA序列片段的特征,基于氨基酸分子中侧链基极性性质把氨基酸分成5大类,计算5大类出现的频率,这种考虑生物意义的特征提取方法不仅考虑碱基的含量,还在一定程度上考虑碱基的排列顺序,应用层次聚类分析方法和BP神经网络法对DNA序列片段进行分类。结果表明,2类算法分类结果精度较高,且一致性也较高。说明这种特征提取法比传统的单纯考虑碱基的特征提取法效果更优。     

网络时代高校思想政治教育的实效性探析

网络时代是一个信息资源丰富的新时期,它开阔了我们的眼界,拓展了我们的知识面,网络信息的发展促进了当今思想文化的新变化.而青年大学生作为利用网络的主群体,它必然会给大学生的思想道德、价值观念带来一定的影响,因此,网络时代的到来给高校思想政治教育带来了机遇和挑战.本文从网络时代的特点出发,探索性地分析了提高高校思想政治教育实效性的新途径.     

An overview of animal science research 1945–2011 through science mapping analysis

The conceptual structure of the field of Animal Science (AS) research is examined by means of a longitudinal science mapping analysis. The whole of the AS research field is analysed, revealing its conceptual evolution. To this end, an automatic approach to detecting and visualizing hidden themes or topics and their evolution across a consecutive span of years was applied to AS publications of the JCR category ‘Agriculture, Dairy & Animal Science’ during the period 1945–2011. This automatic approach was based on a coword analysis and combines performance analysis and science mapping. To observe the conceptual evolution of AS, six consecutive periods were defined: 1945–1969, 1970–1979, 1980–1989, 1990–1999, 2000–2005 and 2006–2011. Research in AS was identified as having focused on ten main thematic areas: ANIMAL‐FEEDING, SMALL‐RUMINANTS, ANIMAL‐REPRODUCTION, DAIRY‐PRODUCTION, MEAT‐QUALITY, SWINE‐PRODUCTION, GENETICS‐AND‐ANIMAL‐BREEDING, POULTRY, ANIMAL‐WELFARE and GROWTH‐FACTORS‐AND‐FATTY‐ACIDS. The results show how genomic studies gain in weight and integrate with other thematic areas. The whole of AS research has become oriented towards an overall framework in which animal welfare, sustainable management and human health play a major role. All this would affect the future structure and management of livestock farming.     

人工神经网络在木材缺陷检测中的应用

采用射线作为检测手段，对木材进行无损检测。在无损检测信号处理和特征构造的基础上。运用特征参数建立了缺陷识别的数学模型，针对无损检测信号的特征，构造了人工神经网络。选用反向传播神经网络模型（BP网络），网络识别所需要的特征参数能够反映木材缺陷的全部特征。     

时滞recurrent神经网络模型的全局渐近稳定性

讨论了时滞recurrent神经网络模型的全局渐近稳定性,通过构造适当的Lyapuov函数,利用线性矩阵不等式,给出了一类常时滞recurrent神经网络的新的充分条件,所获的稳定性条件是时滞相关的,稳定性判别条件更宽松.最后通过一个实例说明方法的可行性.     

吉林省西部可持续发展与森林生态网络建设

吉林省西部为半干旱农牧交错区,以土地沙化、盐碱化和草原退化为主要特征的荒漠化制约着社会经济的可持续发展。本文阐述了该区域的生态经济概况,在总结近半个世纪林业建设正反经验的基础上,论述了吉林省西部森林生态网络建设面临的任务．提出了相关对策和建议。     

可变密度蓄积量的BP网络模型

在森林经营过程中,及时掌握森林资源的现状,预测其发展趋势对森林资源的宏观经营决策和管理十分重要。本文根据金沟岭林场的实际情况,采用人工神经网络方法建立了基于人工神经网络的混交林可变密度蓄积量BP网络模型,采用回归方程适应性检验法分别对模型的拟合效果进行适应性检验,检验结果表明所有模型是实用的,不存在系统偏差,表明用人工神经网络的方法对森林资源进行预测是可行的,可以满足林业生产经营的精度要求。