Size-Energy Relationships in Ecological Communities

Hypotheses that relate body size to energy use are of particular interest in community ecology and macroecology because of their potential to facilitate quantitative predictions about species interactions and to clarify complex ecological patterns. One prominent size-energy hypothesis, the energetic equivalence hypothesis, proposes that energy use from shared, limiting resources by populations or size classes of foragers will be independent of body size. Alternative hypotheses propose that energy use will increase with body size, decrease with body size, or peak at an intermediate body size. Despite extensive study, however, size-energy hypotheses remain controversial, due to a lack of directly-measured data on energy use, a tendency to confound distinct scaling relationships, and insufficient attention to the ecological contexts in which predicted relationships are likely to occur. Our goal, therefore, was to directly evaluate size-energy hypotheses while clarifying how results would differ with alternate methods and assumptions. We comprehensively tested size-energy hypotheses in a vertebrate frugivore guild in a tropical forest in Madagascar. Our test of size-energy hypotheses, which is the first to examine energy intake directly, was consistent with the energetic equivalence hypothesis. This finding corresponds with predictions of metabolic theory and models of energy distribution in ecological communities, which imply that body size does not confer an advantage in competition for energy among populations or size classes of foragers. This result was robust to different assumptions about energy regulation. Our results from direct energy measurement, however, contrasted with those obtained with conventional methods of indirect inference from size-density relationships, suggesting that size-density relationships do not provide an appropriate proxy for size-energy relationships as has commonly been assumed. Our research also provides insights into mechanisms underlying local size-energy relationships and has important implications for predicting species interactions and for understanding the structure and dynamics of ecological communities.     

Assessing the value of the Garden Moth Scheme citizen science dataset: how does light trap type affect catch?

Done well, citizen science projects can gather datasets of a size and scope far larger than would be possible using professional researchers. This study uses data gathered in Britain by the Garden Moth Scheme (GMS). Participants run garden light traps for at least 26 weeks a year and complete garden questionnaires detailing garden habitat and nearby landscape features. We used data exploration and generalised linear modelling (GLM) to investigate whether the data can be used to generate reliable research findings, testing the effect of moth light trap type on moth catch. Robinson traps, then Skinner traps, then Heath traps were found to catch the highest abundance and diversity of moths. Mercury vapour bulbs, then blended light bulbs, then actinic bulbs collected the highest abundance and diversity of moths. The GMS dataset can be used to generate useful and reliable research findings, and can be used in the future to investigate temporal and spatial trends in moth assemblage. Under international law, the use of mercury vapour bulbs will be phased out in coming years, leading to changes in the way moth assemblages are sampled. Information on the relative efficacy of different bulb types will aid the analysis of long‐term moth datasets after these changes.     

Catecholamine inotropes as growth factors for Staphylococcus epidermidis and other coagulase-negative staphylococci

Drugs commonly used in intensive care settings were assayed for their ability to affect the growth of Staphylococcus epidermidis in a minimal salts medium containing 30% serum. Of 28 compounds tested, the inotropic catecholamines adrenaline, dobutamine, dopamine, isoprenaline and noradrenaline significantly stimulated bacterial growth. These drugs, but not structurally similar compounds lacking a dihydroxybenzoyl moiety (such as tyramine, phenylephrine and salbutamol), were able to remove iron from iron-saturated transferrin and to supply transferrin-bound 55Fe to S. epidermidis cells. Similar results were observed with a range of coagulase-negative staphylococci associated with line infections, but not with Staphylococcus aureus (including MRSA).     

Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist

A recent increase in studies of β diversity has yielded a confusing array of concepts, measures and methods. Here, we provide a roadmap of the most widely used and ecologically relevant approaches for analysis through a series of mission statements. We distinguish two types of β diversity: directional turnover along a gradient vs. non-directional variation. Different measures emphasize different properties of ecological data. Such properties include the degree of emphasis on presence/absence vs. relative abundance information and the inclusion vs. exclusion of joint absences. Judicious use of multiple measures in concert can uncover the underlying nature of patterns in β diversity for a given dataset. A case study of Indonesian coral assemblages shows the utility of a multi-faceted approach. We advocate careful consideration of relevant questions, matched by appropriate analyses. The rigorous application of null models will also help to reveal potential processes driving observed patterns in β diversity.     

Neural mass models as a tool to investigate neural dynamics during seizures

Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.     

Effects of calcium and calcium chelators on growth and morphology of Escherichia coli L-form NC-7

Growth of a wall-less, L-form of Escherichia coli specifically requires calcium, and in its absence, cells ceased dividing, became spherical, swelled, developed large vacuoles, and eventually lysed. The key cell division protein, FtsZ, was present in the L-form at a concentration five times less than that in the parental strain. One interpretation of these results is that the L-form possesses an enzoskeleton partly regulated by calcium.     

A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery

This work describes a generalized method for classifying motor-related neural signals for a brain-computer interface (BCI), based on a stochastic machine learning method. The method differs from the various feature extraction and selection techniques employed in many other BCI systems. The classifier does not use extensive a-priori information, resulting in reduced reliance on highly specific domain knowledge. Instead of pre-defining features, the time-domain signal is input to a population of multi-layer perceptrons (MLPs) in order to perform a stochastic search for the best structure. The results showed that the average performance of the new algorithm outperformed other published methods using the Berlin BCI IV (2008) competition dataset and was comparable to the best results in the Berlin BCI II (2002–3) competition dataset. The new method was also applied to electroencephalography (EEG) data recorded from five subjects undertaking a hand squeeze task and demonstrated high levels of accuracy with a mean classification accuracy of 78.9% after five-fold cross-validation. Our new approach has been shown to give accurate results across different motor tasks and signal types as well as between subjects.     

Microbial endocrinology: how stress influences susceptibility to infection

A holistic approach to understanding the mechanisms by which stress influences the pathogenesis of infectious disease has resulted in the development of the field of microbial endocrinology. This transdisciplinary field represents the intersection of microbiology with mammalian endocrinology and neurophysiology, and is based on the tenet that microorganisms have evolved systems for using neurohormones, which are widely distributed throughout nature, as environmental cues to initiate growth and pathogenic processes. This review reveals that responsiveness to human stress hormones is widespread in the microbial world and documents recent advances in microbial endocrinology.