Positive interactions, including intraspecies cooperation and interspecies mutualisms, play crucial roles in shaping the structure and function of many ecosystems, ranging from plant communities to the human microbiome. While the evolutionary forces that form and maintain positive interactions have been investigated extensively, the influence of positive interactions on the ability of species to adapt to new environments is still poorly understood. Here, we use numerical simulations and theoretical analyses to study how positive interactions impact the likelihood that populations survive after an environment deteriorates, such that survival in the new environment requires quick adaptation via the rise of new mutants-a scenario known as evolutionary rescue. We find that the probability of evolutionary rescue in populations engaged in positive interactions is reduced significantly. In cooperating populations, this reduction is largely due to the fact that survival may require at least a minimal number of individuals, meaning that adapted mutants must arise and spread before the population declines below this threshold. In mutualistic populations, the rescue probability is decreased further due to two additional effects-the need for both mutualistic partners to adapt to the new environment, and competition between the two species. Finally, we show that the presence of cheaters reduces the likelihood of evolutionary rescue even further, making it extremely unlikely. These results indicate that while positive interactions may be beneficial in stable environments, they can hinder adaptation to changing environments and thereby elevate the risk of population collapse. Furthermore, these results may hint at the selective pressures that drove co-dependent unicellular species to form more adaptable organisms able to differentiate into multiple phenotypes, including multicellular life.

The effect of environmental fluctuations is a major question in ecology. While it is widely accepted that fluctuations and other types of disturbances can increase biodiversity, there are fewer examples of other types of outcomes in a fluctuating environment. Here we explore this question with laboratory microcosms, using cocultures of two bacterial species, P. putida and P. veronii. At low dilution rates we observe competitive exclusion of P. veronii, whereas at high dilution rates we observe competitive exclusion of P. putida. When the dilution rate alternates between high and low, we do not observe coexistence between the species, but rather alternative stable states, in which only one species survives and initial species' fractions determine the identity of the surviving species. The Lotka-Volterra model with a fluctuating mortality rate predicts that this outcome is independent of the timing of the fluctuations, and that the time-averaged mortality would also lead to alternative stable states, a prediction that we confirm experimentally. Other pairs of species can coexist in a fluctuating environment, and again consistent with the model we observe coexistence in the time-averaged dilution rate. We find a similar time-averaging result holds in a three-species community, highlighting that simple linear models can in some cases provide powerful insight into how communities will respond to environmental fluctuations.

High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.

The efficiency of the time synchronization service in wireless sensor networks is tightly connected to the design of the radio, the quality of the clocking hardware, and the synchronization algorithm employed. While improvements can be made on all levels of the system, over the last few years most work has focused on the algorithmic level to minimize message exchange and in radio architectures to provide accurate time-stamping mechanisms. Surprisingly, the influences of the underlying clock system and its impact on the overall synchronization accuracy has largely been unstudied. In this work, we investigate the impact of the clocking subsystem on the time synchronization service and address, in particular, the influence of changes in environmental temperature on clock drift in highly duty-cycled wireless sensor nodes. We also develop formulas that help the system architect choose the optimal resynchronization period to achieve a given synchronization accuracy. We find that the synchronization accuracy has a two region behavior. In the first region, the synchronization accuracy is limited by quantization error, while int he second region changes in environmental temperature impact the achievable accuracy. We verify our analytic results in simulation and real hardware experiments.

Compressive Sensing (CS) is a recently developed mechanism that allows signal acquisition and compression to be performed in one inexpensive step so that the sampling process itself produces a compressed version of the signal. This significantly improves systemic energy efficiency because the average sampling rate can be considerably reduced and explicit compression eliminated.

Biochemical activities in sewer pipes generate various volatile substances that lead to several serious problems such as malodor complaints and lawsuits, concrete and metal corrosion, increased operational costs, and health risks. Frequent inspections are critical to maintain sewer health, yet are extremely expensive given the extent of the sewer system and the "unfriendliness" of the environment. In this paper we propose SewerSnort, a low cost, unmanned, fully automated in-sewer gas monitoring system. A sensor float is introduced at the upstream station and drifts to the end pumping station, collecting location tagged gas measurements. The retrieved SewerSnort provides an accurate gas exposure profile to be used for preventive maintenance and/or repair. The key innovations of SewerSnort are the fully automated, end-to-end monitoring solution and the low energy self localizing strategy. From the implementation standpoint, the key enablers are the float mechanical design that fits the sewer constraints and the embedded sensor design that matches the float form factor and complies with the tight energy constraints. Experiments based on a dry land emulator demonstrate the feasibility of the SewerSnort concept, in particular, the localization technique and the embedded sensor design.

Time keeping and synchronization are important services for networked and embedded systems. High quality timing information allows embedded network nodes to provide accurate time-stamping, fast localization, efficient duty cycling schedules, and other basic but essential functions - all of which are required for low power operation.

Water is nature's most precious resource and growing demand is pushing fresh water supplies to the brink of non-renewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with fine-grained real-time information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users.

Time synchronization is an essential service in distributed computing and control systems. It is used to enable tasks such as synchronized data sampling and accurate time-of-flight estimation, which can be used to locate nodes. The deviation in nodes' knowledge of time and inter-node resynchronization rate are affected by three sources of time stamping errors: network wireless communication delays, platform hardware and software delays, and environment-dependent frequency drift characteristics of the clock source. The focus of this work is on the last source of error, the clock source, which becomes a bottleneck when either required time accuracy or available energy budget and bandwidth (and thus feasible resynchronization rate) are too stringent. Traditionally, this has required the use of expensive clock sources (such as temperature compensation using precise sensors and calibration models) that are not cost-effective in low-end wireless sensor nodes. Since the frequency of a crystal is a product of manufacturing and environmental parameters, we describe an approach that exploits the subtle manufacturing variation between a pair of inexpensive oscillators placed in close proximity to algorithmically compensate for the drift produced by the environment. The algorithm effectively uses the oscillators themselves as a sensor that can detect changes in frequency caused by a variety of environmental factors. We analyze the performance of our approach using behavioral models of crystal oscillators in our algorithm simulation. Then we apply the algorithm to an actual temperature dataset collected at the James Wildlife Reserve in Riverside County, California, and test the algorithms on a waveform generator based testbed. The result of our experiments show that the technique can effectively improve the frequency stability of an inexpensive uncompensated crystal 5 times with the potential for even higher gains in future implementations.

No abstract available.

Sustainable operation of battery powered wireless embedded systems (such as sensor nodes) is a key challenge, and considerable research effort has been devoted to energy optimization of such systems. Environmental energy harvesting, in particular solar based, has emerged as a viable technique to supplement battery supplies. However, designing an efficient solar harvesting system to realize the potential benefits of energy harvesting requires an in-depth understanding of several factors. For example, solar energy supply is highly time varying and may not always be sufficient to power the embedded system. Harvesting components, such as solar panels, and energy storage elements, such as batteries or ultracapacitors, have different voltage-current characteristics, which must be matched to each other as well as the energy requirements of the system to maximize harvesting efficiency. Further, battery non-idealities, such as self-discharge and round trip efficiency, directly affect energy usage and storage decisions. The ability of the system to modulate its power consumption by selectively deactivating its sub-components also impacts the overall power management architecture. This paper describes key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and presents the design, implementation, and performance evaluation of Heliomote, our prototype that addresses several of these issues. Experimental results demonstrate that Heliomote, which behaves as a plug-in to the Berkeley/Crossbow motes and autonomously manages energy harvesting and storage, enables near-perpetual, harvesting aware operation of the sensor node.

No abstract available.

No abstract available.

No abstract available.