Species constancy depends on plot size – a problem for vegetation classification and how it can be solved

Question: While it is well known that species richness depends on plot size, it is not generally recognised that the same must be true for constancy. Accordingly, many authors use varying plot sizes when classifying vegetation based on the comparison of constancies between groups of plots. We ask whether the constancy‐area relationship follows a general rule, how strong the effect of plot sizes is on constancies, and if it is possible to correct constancies for area. Location: For empirical evaluation, we use data from plant communities in the Czech Republic, Sweden and Russia. Methods: To assess the potential influence of differences in plot size on constancies, we develop a mathematical model. Then, we use series of nested plot species richness data from a wide range of community types (herbaceous and forest) to determine the parameters of the derived function and to test how much the shape of the constancy‐area relationship depends on taxa or vegetation types. Results: Generally, the constancy‐area relationship can be described by C (A)=1?(1?C₀)^{(A/A0)^d}, with C being constancy, A area, C₀ known constancy on a specific area A₀, and d a damping parameter accounting for spatial autocorrelation. As predicted by this function, constancies in plant communities always varied from values near 0% to near 100% if plot sizes were changed sufficiently. For the studied vegetation types, a two‐ to fourfold increase in plot size resulted in a change of conventional constancy classes, i.e. an increase of constancy by 20% or more. Conclusions: Vegetation classification, which largely relies on constancy values, irrespective of whether traditional or modern fidelity definitions are used, is strongly prone to distorting scale effects when relevés of different plot sizes are combined in studies. The constancy‐area functions presented allow an approximate transformation of constancies to other plot sizes but are flawed by idiosyncrasies in taxa and vegetation types. Thus, we conclude that the best solution for future surveys is to apply uniform plot sizes within a few a priori delimited formations and to determine diagnostic species only within these formations. Finally, we suggest that more detailed analyses of constancy‐area relationships can contribute to a better understanding of species‐area relationships because the latter are the summation of the first for all species.     

视觉皮层下通路与拓扑知觉

研究表明，视觉通路除了经典的视觉皮层通路外，还有一条“古老的”、快速的皮层下通路负责在有意识和无意识状态下快速处理与情绪相关的信息。皮层下视觉通路由上丘、枕核和杏仁核组成，而且不经过初级视觉皮层。我们前期研究表明，初级视觉皮层与拓扑知觉信息加工没有关系，而皮层下视觉通路负责处理视觉拓扑信息。基于这些发现，我们认为，在早期视觉中，大脑检测涉及生命攸关的信号，这些信号告诉大脑：环境中有物体出现或消失，使大脑进入警戒状态，这对物种的生存至关重要。因此，在早期视觉中，需要检测的要素只是物体的“出现”和“消失”，而不是“纹理”、“形状”等。“出现”和“消失”都是拓扑特征的变化。拓扑感知和皮层下视觉通路的存在可能是早期预警的神经基础。在灵长类动物中，视网膜外周区域主要由视杆细胞构成，该区域接收的视觉信息主要通过皮层下视觉通路进行处理；视网膜中心区域（即中央凹）主要由视锥细胞构成，视觉空间分辨率变得非常高，该区域视觉信息处理主要是由视觉皮层负责。研究表明，由视杆细胞构成的视网膜是个“古老”结构，在1亿多年前就出现了；而视锥细胞构成的视网膜类型较为“年轻”，在5 000万年前才出现。所以，我们的视网膜从“古老”结构演化到“年轻”结构至少用了5 000万年的时间，它是由一个古老结构和一个年轻结构共同组成的“嵌合体”。当我们讨论皮层下通路存在的意义时，我们也应该结合皮层通路的功能来统一考虑。     

燕山地区访花昆虫多样性及其影响因子

为了解燕山地区访花昆虫的群落结构及与其生境类型、干扰程度、海拔之间的关系, 本文采用样线法和灯诱法于2019年、2020年每年的7-8月对该地区湿地、森林、灌丛、草地、农田5种生境, 不同海拔梯度(0-1,200 m)的访花昆虫进行了采集。共采集访花昆虫1,306头, 隶属7目44科153种, 其中鳞翅目昆虫物种数最多, 半翅目昆虫个体数最多。灌丛生境的访花昆虫多样性最高。在中低海拔200-400 m段, Shannon-Wiener多样性指数、Margalef丰富度指数和Simpson优势度指数均最高。双变量回归结果表明, Shannon-Wiener多样性指数和Margalef丰富度指数分别与最暖季降水量和年降水量显著正相关(P < 0.05)。冗余分析(redundancy analysis)结果表明, 环境因子显著影响访花昆虫多样性, 但不同测度之间存在一定差异。温度和湿度均与Pielou均匀度指数呈正相关, 与Shannon-Wiener多样性指数、Margalef丰富度指数和Simpson优势度指数呈负相关; 弱干扰和中干扰程度对访花昆虫多样性影响最小, 科学管理农牧活动是保护访花昆虫多样性的关键。     

大中微量元素组合对水稻产量和品质影响的灰色综合评判

本文运用灰色系统理论，探讨了大中微量元素组合对水稻产量和品质的影响，并对其进行了灰色关联评判，结果表明复4S1Fe1Cu1和复2S2Fe1Cu2两个组合，对水稻产量和品质影响最为明显，并与田间试验情况一致。     

Field evaluation of heat units for predicting crop residue carbon and nitrogen mineralization

More efficient fertilization practices could conceivably be developed if nutrient availability from manures and crop residues could be accurately predicted under field conditions. Heat units were recently found useful for describing the combined thermal and temporal relationships with papermill sludge C and N mineralization. This study was conducted to describe C and N mineralization of corn (Zea mays L. cv. ‘King 1113’), lupin (Lupinus albus L. cv. ‘ultra’), and potato (Solanum tuberosum L. cv. ‘russet burbank’) residues and to field test the utility of heat units in predicting these processes. Residues of each crop were applied to soil microplots in May, June, and July of 1987. Carbon mineralization was monitored by periodic capture of evolved CO₂ in alkali traps. Nitrogen mineralization-immobilization processes were followed by frequent sampling for KCl extractable inorganic N. Net N mineralization from the potato residue occurred almost immediately after residue application, apparently owing to its relatively low C/N of 15/1. Net N mineralization from the corn and lupin residues began 119, 99, and 317 d after application in the May, June, and July application months, respectively. These respective times represented 2346, 1990, and 2360 degree days after application. Heat units appear to provide a mathematically simple, pragmatic approach for predicting crop residue N mineralization under field climatic conditions.     

关于《一类具时滞的神经网络模型的收敛性》的注记

本文研究了一类具有时滞的神经网络模型