The Bio3D packages for structural bioinformatics

Bio3D is a family of R packages for the analysis of biomolecular sequence, structure, and dynamics. Major functionality includes biomolecular database searching and retrieval, sequence and structure conservation analysis, ensemble normal mode analysis, protein structure and correlation network analysis, principal component, and related multivariate analysis methods. Here, we review recent package developments, including a new underlying segregation into separate packages for distinct analysis, and introduce a new method for structure analysis named ensemble difference distance matrix analysis (eDDM). The eDDM approach calculates and compares atomic distance matrices across large sets of homologous atomic structures to help identify the residue wise determinants underlying specific functional processes. An eDDM workflow is detailed along with an example application to a large protein family. As a new member of the Bio3D family, the Bio3D‐eddm package supports both experimental and theoretical simulation‐generated structures, is integrated with other methods for dissecting sequence‐structure–function relationships, and can be used in a highly automated and reproducible manner. Bio3D is distributed as an integrated set of platform independent open source R packages available from: http://thegrantlab.org/bio3d/ .     

Strategic Habitat Conservation for Beach Mice: Estimating Management Scenario Efficiencies

The Perdido Key beach mouse (Peromyscus polionotus trissyllepsis), Choctawhatchee beach mouse (P. p. allophrys), and St. Andrew beach mouse (P. p. peninsularis) are 3 federally endangered subspecies that inhabit coastal dunes of Alabama and Florida, USA. Conservation opportunities for these subspecies are limited and costly. Consequently, well-targeted efforts are required to achieve their downlisting criteria. To aid the development of targeted management scenarios that are designed to achieve downlisting criteria, we developed a Bayesian network model that uses habitat characteristics to predict the probability of beach mouse presence at a 30-m resolution across a portion of the Florida Panhandle. We then designed alternative management scenarios for a variety of habitat conditions for coastal dunes. Finally, we estimated how much area is needed to achieve the established downlisting criterion (i.e., habitat objective) and the amount of effort needed to achieve the habitat objective (i.e., management efficiency). The results suggest that after 7 years of post-storm recolonization, habitat objectives were met for Perdido Key (within its Florida critical habitat) and Choctawhatchee beach mice. The St. Andrew beach mouse required 5.14 km² of additional critical habitat to be protected and occupied. The St. Andrew beach mouse habitat objective might be achieved by first restoring protected critical habitat to good dune conditions and then protecting or restoring the unprotected critical habitat with the highest predicted probability of beach mouse presence. This scenario provided a 28% increase in management efficiency compared to a scenario that randomly protected or restored undeveloped unprotected critical habitat. In total, when coupled with established downlisting criteria, these quantitative and spatial decision support tools could provide insight into how much habitat is available, how much more is needed, and targeted conservation or restoration efforts that might efficiently achieve habitat objectives. © 2020 The Wildlife Society.     

Socially Defined Subpopulations Reveal Demographic Variation in a Giraffe Metapopulation

Populations are typically defined as spatially contiguous sets of individuals, but large populations of social species can be composed of discrete social communities that often overlap in space. Masai giraffes (Giraffa camelopardalis tippelskirchi) of Tanzania live in distinct social subpopulations that overlap spatially, enabling us to simultaneously explore environmental and social factors correlated with demographic variation in a metapopulation of >1,400 adult females and calves. We considered statistically distinct communities in the social network as subpopulations and tested for variation among the 10 subpopulations in adult female survival, calf survival, and reproductive rate (calf-to-adult female ratio). We then related variation in demographic rates among subpopulations to differences in vegetation, soil type, proximity to 2 types of human settlements, local giraffe population density, and social metrics of relationship strength and exclusivity among adult females. We did not find any among-subpopulation effects on adult female survival, suggesting adult female survival is buffered against environmental heterogeneity among subpopulations. Variation in calf demographic rates among subpopulations were correlated with vegetation, soils, anthropogenic factors, and giraffe population density but not with adult female relationship metrics, despite substantial spatial overlap. Subpopulations with more dense bushlands in their ranges had lower calf survival probabilities, and those closer to human settlements had higher reproductive rates, possibly because of spatial gradients in natural predation. Reproductive rates were higher in subpopulations with more volcanic soils, and calf survival probabilities were greater in subpopulations with higher local adult female densities, possibly related to higher-quality habitat associated with fertile soils or lower predation risk, or to greater competitive ability. The variation in fitness among subpopulations suggests that giraffes do not move unhindered among resource patches to equalize reproductive success, as expected according to an ideal free distribution. The differences in calf survival and reproductive rates could rather indicate intercommunity differences in competitive ability, perception, learning, or experience. Our approach of comparing demography among spatially overlapping yet distinct socially defined subpopulations provides a biologically meaningful way to quantify environmental and social factors influencing fine-scale demographic variation for social species. © 2021 The Wildlife Society.     

陆地生态系统环境控制实验的研究方法及技术体系

生物及生态系统与环境变化间的反馈关系及其过程机制是生态学研究的重要内容。不同类型的生物环境因素控制实验以及大尺度的联网野外控制实验被认为是认识生态系统响应和适应环境变化过程机制、精细定量表达的有效手段及认知过程的加速器。近年来发展了大型野外物理模拟实验装置网络(如ECOTRON)、生态系统分析与实验平台(AnaEE)、国际干旱实验研究网络(Drought Network)、氮沉降联合实验网络(Nutrient Network),以及基于各区域性生态观测实验站的联网控制实验(如USA-ILTER)。发展大陆尺度联网实验研究平台事业正日益受到学术界的重视,将会在认知生态系统环境响应过程机制方面发挥更重要的作用。基于以上背景,本文综述了生态系统环境控制实验的研究方法和实验体系的发展,明确指出各种类型的生物环境控制实验需要形成联合协作体系,共同解决生态系统对环境变化的响应及适应的基本科学问题。目前的控制实验包括: 1) 实验室封闭装置内的生物生理生态学控制实验;2) 野外实验场的半开放部分环境要素控制实验;3) 近自然状态的野外环境控制实验;以及4) 基于野外生态站的联网控制实验。进而,本文还深入讨论了陆地生态系统的环境响应及适应过程机制实验系统设计的发展趋势,分析了基于大尺度自然环境梯度实验及生态站尺度的要素控制实验的优势,提出了整合两种实验技术、发展新一代的野外联网实验体系的科学设想,讨论了基于野外联网控制实验的研究体系,论证了研究生态系统对环境变化短期响应和长期适应的规律和机制、生态系统环境响应定量表达的技术途径。若本文提出的控制实验体系设计方案能够得以实施,必将大大促进我国乃至全球生态系统和环境变化科学的研究水平,对我国应对气候变化和生态环境建设具有重要的科学意义。     

既主内政, 又辖外交——以PHR为中心的基因网络调控植物-菌根真菌的共生

磷是植物生长发育必需的大量矿质营养元素, 但自然界大部分土壤都存在严重缺磷的问题。为了适应这一营养逆境, 植物演化出一系列低磷胁迫应答反应。通过改变基因的转录水平调控低磷胁迫应答反应, 而转录因子PHR1在调控植物对低磷胁迫的转录响应中起关键作用。此外, 大部分陆生植物还能与丛枝菌根真菌建立共生关系, 通过丛枝菌根真菌更有效地从土壤中获取磷元素。最近, 中国科学院分子植物科学卓越创新中心王二涛研究组发现, 以PHR为中心的转录调控网络控制植物-丛枝菌根真菌共生的建立。因此, PHR不但在维持植物细胞自身的磷稳态中发挥作用, 而且参与植物与外界微生物的相互作用, 为植物有效地从环境中获得磷元素提供了另外一条途径。