Bacterial adaptation is constrained in complex communities

© 2020, The Author(s). A major unresolved question is how bacteria living in complex communities respond to environmental changes. In communities, biotic interactions may either facilitate or constrain evolution depending on whether the interactions expand or contract the range of ecological opportunities. A fundamental challenge is to understand how the surrounding biotic community modifies evolutionary trajectories as species adapt to novel environmental conditions. Here we show that community context can dramatically alter evolutionary dynamics using a novel approach that ‘cages’ individual focal strains within complex communities. We find that evolution of focal bacterial strains depends on properties both of the focal strain and of the surrounding community. In particular, there is a stronger evolutionary response in low-diversity communities, and when the focal species have a larger genome and are initially poorly adapted. We see how community context affects resource usage and detect genetic changes involved in carbon metabolism and inter-specific interaction. The findings demonstrate that adaptation to new environmental conditions should be investigated in the context of interspecific interactions

The Passive Yet Successful Way of Planktonic Life: Genomic and Experimental Analysis of the Ecology of a Free-Living Polynucleobacter Population

Background: The bacterial taxon Polynucleobacter necessarius subspecies asymbioticus represents a group of planktonic freshwater bacteria with cosmopolitan and ubiquitous distribution in standing freshwater habitats. These bacteria comprise,1 % to 70 % (on average about 20%) of total bacterioplankton cells in various freshwater habitats. The ubiquity of this taxon was recently explained by intra-taxon ecological diversification, i.e. specialization of lineages to specific environmental conditions; however, details on specific adaptations are not known. Here we investigated by means of genomic and experimental analyses the ecological adaptation of a persistent population dwelling in a small acidic pond. Findings: The investigated population (F10 lineage) contributed on average 11 % to total bacterioplankton in the pond during the vegetation periods (ice-free period, usually May to November). Only a low degree of genetic diversification of the population could be revealed. These bacteria are characterized by a small genome size (2.1 Mb), a relatively small number of genes involved in transduction of environmental signals, and the lack of motility and quorum sensing. Experiments indicated that these bacteria live as chemoorganotrophs by mainly utilizing low-molecular-weight substrates derived from photooxidation of humic substances. Conclusions: Evolutionary genome streamlining resulted in a highly passive lifestyle so far only known among free-living bacteria from pelagic marine taxa dwelling in environmentally stable nutrient-poor off-shore systems. Surprisingly, such a lifestyle is also successful in a highly dynamic and nutrient-richer environment such as the water column of the investigate

Resource-dependent attenuation of species interactions during bacterial succession

Bacterial communities are vital for many economically and ecologically important processes. The role of bacterial community composition in determining ecosystem functioning depends critically on interactions among bacterial taxa. Several studies have shown that, despite a predominance of negative interactions in communities, bacteria are able to display positive interactions given the appropriate evolutionary or ecological conditions. We were interested in how interspecific interactions develop over time in a naturalistic setting of low resource supply rates. We assembled aquatic bacterial communities in microcosms and assayed the productivity (respiration and growth) and substrate degradation while tracking community composition. The results demonstrated that while bacterial communities displayed strongly negative interactions during the early phase of colonisation and acclimatisation to novel biotic and abiotic factors, this antagonism declined over time towards a more neutral state. This was associated with a shift from use of labile substrates in early succession to use of recalcitrant substrates later in succession, confirming a crucial role of resource dynamics in linking interspecific interactions with ecosystem functioning

Flask : an architecture supporting concurrent distributed persistent applications

Submitted to BNCOD96 The work was supported by ESPRIT III BRA 6309 — FIDE2 and EPSRC Grant GR/J67611Distributed application systems have become a popular and provenly viable computing paradigm. There are a number of reasons for this such as: the geographical dispersal of information; the improved reliability of multiple computer systems; and the possibility of concurrent execution of applications. As yet no single model of distribution has been pervasive and since the impact of failure semantics varies with the software architecture of applications, it is unlikely that one model will ever dominate. It is difficult to assess or even to compare the attributes of different models especially when run over the same data. This is often made more difficult in that most implementations of distributed models are closed systems with built-in protocols, failure reporting and concurrency control. The Flask architecture, presented here, takes the approach of providing a layered architecture which has the flexibility to support different models of distribution that can run over the same data. To demonstrate the feasibility of Flask an example distributed application is described using the architecture.Postprin

The DataSafe Failure Recovery Mechanism in the Flask Architecture

A major design goal of the Flask architecture is to separate the mechanisms of concurrency control and recovery management in database programming systems. This paper describes the DataSafe component of Flask, which is the second recovery mechanism to be implemented within the architecture and therefore provides a proof of concept. The DataSafe is closely based on the DB Cache mechanism, modified to fit into the Flask architecture. The major modification comprises the use of a separate safe map which allows pages of recovery data to be block aligned and affords opportunities for efficiency gains during recovery. The page-level locking implicit in the DB Cache is lifted from the DataSafe, permitting concurrency control and recovery to be independent. Keywords concurrency, recovery, persistent stores 1 Introduction Flask [11] is a layered architecture which has the flexibility to support different models of concurrency and different recovery mechanisms over the same data. The architec..

The MaStA I/O Cost Model and its Validation Strategy

Crash recovery in database systems aims to provide an acceptable level of protection from failure at a given engineering cost. A large number of recovery mechanisms are known, and have been compared both analytically and empirically. However, recent trends in computer hardware present different engineering tradeoffs in the design of recovery mechanisms. In particular, the comparative improvement in the speed of processors over disks suggests that disk I/O activity is the dominant expense. Furthermore, the improvement of disk transfer time relative to seek time has made patterns of disk access more significant. The contribution of the MaStA (Ma ssachusetts St Andrews) cost model is that it is structured independently of machine architectures and application workloads. It determines costs in terms of I/O categories, access patterns and application workload parameters. The main features of the model are: . Cost is based upon a probabilistic estimation of disk activity, broken down into s..

Flask:an architecture supporting concurrent distributed persistent applications

Distributed application systems have become a popular and provenly viable computing paradigm. There are a number of reasons for this such as: the geographical dispersal of information; the improved reliability of multiple computer systems; and the possibility of concurrent execution of applications. As yet no single model of distribution has been pervasive and since the impact of failure semantics varies with the software architecture of applications, it is unlikely that one model will ever dominate. It is difficult to assess or even to compare the attributes of different models especially when run over the same data. This is often made more difficult in that most implementations of distributed models are closed systems with built-in protocols, failure reporting and concurrency control. The Flask architecture, presented here, takes the approach of providing a layered architecture which has the flexibility to support different models of distribution that can run over the same data. To demonstrate the feasibility of Flask an example distributed application is described using the architecture