The stability of ecosystems: a brief overview of the paradox of enrichment

In theory, enrichment of resource in a predator-prey model leads to destabilization of the system, thereby collapsing the trophic interaction, a phenomenon referred to as "the paradox of enrichment". After it was first proposed by Rosenzweig (1971), a number of subsequent studies were carried out on this dilemma over many decades. In this article, we review these theoretical and experimental works and give a brief overview of the proposed solutions to the paradox. The mechanisms that have been discussed are modifications of simple predator-prey models in the presence of prey that is inedible, invulnerable, unpalatable and toxic. Another class of mechanisms includes an incorporation of a ratio-dependent functional form, inducible defence of prey and density-dependent mortality of the predator. Moreover, we find a third set of explanations based on complex population dynamics including chaos in space and time. We conclude that, although any one of the various mechanisms proposed so far might potentially prevent destabilization of the predator-prey dynamics following enrichment, in nature different mechanisms may combine to cause stability, even when a system is enriched. The exact mechanisms, which may differ among systems, need to be disentangled through extensive field studies and laboratory experiments coupled with realistic theoretical models.

Assessing and monitoring impacts of genetically modified plants on agro-ecosystems: the approach of AMIGA project

The environmental impacts of genetically modified crops is still a controversial issue in Europe. The overall risk assessment framework has recently been reinforced by the European Food Safety Authority(EFSA) and its implementation requires harmonized and efficient methodologies. The EU-funded research project AMIGA − Assessing and monitoring Impacts of Genetically modified plants on Agro-ecosystems − aims to address this issue, by providing a framework that establishes protection goals and baselines for European agro-ecosystems, improves knowledge on the potential long term environmental effects of genetically modified (GM) plants, tests the efficacy of the EFSA Guidance Document for the Environmental Risk Assessment, explores new strategies for post market monitoring, and provides a systematic analysis of economic aspects of Genetically Modified crops cultivation in the EU. Research focuses on ecological studies in different EU regions, the sustainability of GM crops is estimated by analysing the functional components of the agro-ecosystems and specific experimental protocols are being developed for this scope.

Mismatch Negativity (MMN) reveals inefficient auditory ventral stream function in chronic auditory comprehension impairments

Background: Auditory discrimination is significantly impaired in Wernicke’s aphasia (WA) and thought to be causatively related to the language comprehension impairment which characterises the condition. This study used mismatch negativity (MMN) to investigate the neural responses corresponding to successful and impaired auditory discrimination in WA. Methods: Behavioural auditory discrimination thresholds of CVC syllables and pure tones were measured in WA (n=7) and control (n=7) participants. Threshold results were used to develop multiple-deviant mismatch negativity (MMN) oddball paradigms containing deviants which were either perceptibly or non-perceptibly different from the standard stimuli. MMN analysis investigated differences associated with group, condition and perceptibility as well as the relationship between MMN responses and comprehension (within which behavioural auditory discrimination profiles were examined). Results: MMN waveforms were observable to both perceptible and non-perceptible auditory changes. Perceptibility was only distinguished by MMN amplitude in the PT condition. The WA group could be distinguished from controls by an increase in MMN response latency to CVC stimuli change. Correlation analyses displayed relationship between behavioural CVC discrimination and MMN amplitude in the control group, where greater amplitude corresponded to better discrimination. The WA group displayed the inverse effect; both discrimination accuracy and auditory comprehension scores were reduced with increased MMN amplitude. In the WA group, a further correlation was observed between the lateralisation of MMN response and CVC discrimination accuracy; the greater the bilateral involvement the better the discrimination accuracy. Conclusions: The results from this study provide further evidence for the nature of auditory comprehension impairment in WA and indicate that the auditory discrimination deficit is grounded in a reduced ability to engage in efficient hierarchical processing and the construction of invariant auditory objects. Correlation results suggest that people with chronic WA may rely on an inefficient, noisy right hemisphere auditory stream when attempting to process speech stimuli.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

How well can we model stream phosphorus concentrations in agricultural catchments?

Mechanistic catchment-scale phosphorus models appear to perform poorly where diffuse sources dominate. We investigate the reasons for this for one model, INCA-P, testing model output against 18 months of daily data in a small Scottish catchment. We examine key model processes and provide recommendations for model improvement and simplification. Improvements to the particulate phosphorus simulation are especially needed. The model evaluation procedure is then generalised to provide a checklist for identifying why model performance may be poor or unreliable, incorporating calibration, data, structural and conceptual challenges. There needs to be greater recognition that current models struggle to produce positive Nash–Sutcliffe statistics in agricultural catchments when evaluated against daily data. Phosphorus modelling is difficult, but models are not as useless as this might suggest. We found a combination of correlation coefficients, bias, a comparison of distributions and a visual assessment of time series a better means of identifying realistic simulations.

Cadaver decomposition in terrestrial ecosystems

A dead mammal (i.e. cadaver) is a high quality resource (narrow carbon:nitrogen ratio, high water content) that releases an intense, localised pulse of carbon and nutrients into the soil upon decomposition. Despite the fact that as much as 5,000 kg of cadaver can be introduced to a square kilometre of terrestrial ecosystem each year, cadaver decomposition remains a neglected microsere. Here we review the processes associated with the introduction of cadaver-derived carbon and nutrients into soil from forensic and ecological settings to show that cadaver decomposition can have a greater, albeit localised, effect on belowground ecology than plant and faecal resources. Cadaveric materials are rapidly introduced to belowground floral and faunal communities, which results in the formation of a highly concentrated island of fertility, or cadaver decomposition island (CDI). CDIs are associated with increased soil microbial biomass, microbial activity (C mineralisation) and nematode abundance. Each CDI is an ephemeral natural disturbance that, in addition to releasing energy and nutrients to the wider ecosystem, acts as a hub by receiving these materials in the form of dead insects, exuvia and puparia, faecal matter (from scavengers, grazers and predators) and feathers (from avian scavengers and predators). As such, CDIs contribute to landscape heterogeneity. Furthermore, CDIs are a specialised habitat for a number of flies, beetles and pioneer vegetation, which enhances biodiversity in terrestrial ecosystems.

Changes in sewage sludge carbon forms along a treatment stream

The behaviour and fate of macronutrients and pollutants in sewage sludge applied to the land are affected by the chemical composition of the sludge organic matter, which in turn is influenced by both sewage source and by sewage treatment processes. In this study, 13C nuclear magnetic resonance (NMR) spectroscopy was used to characterise the organic matter of sludges collected at three different points along the treatment stream of a municipal sewage works with a domestic catchment. Sludge at the first point, an undigested liquid (UL) sludge, had a substantially different composition to the anaerobically digested (AD) and dewatered sludge cake (DC) materials, which were similar to each other. In particular, the UL sludge contained more alkyl C than the AD or DC sludges. All three sludges were found to contain mobile alkyl C that is poorly observed using the cross polarisation (CP) technique, necessitating the use of the less sensitive, but more quantitatively reliable direct polarisation (DP) technique to obtain accurate distributions of C types.

Soil phosphorus dynamics and phytoavailability from sewage sludge at different stages in a treatment stream

The effect of different stages of sewage sludge treatment on phosphorus (P) dynamics in amended soils was determined using samples of undigested liquid (UL), anaerobically digested liquid (AD) and dewatered anaerobically digested (DC) sludge. Sludges were taken from three points in the same treatment stream and applied to a sandy loam soil in field-based mesocosms at 4, 8 and 16t ha−1 dry solids. Mesocosms were sown with perennial ryegrass (Lolium perenne cv. Melle), and the sward was harvested after 35 and 70 days to determine yield and foliar P concentration. Soils were also sampled during this period to measure P transformations and the activities of acid phosphomonoesterase and phosphodiesterase. Data show that the AD amended soils had the greatest plant-available and foliar P content up to the second harvest, but the UL amended soils had the greatest enzyme activity. Characterisation of control and 16t ha−1 soils and sludge using solution 31P nuclear magnetic resonance (NMR) spectroscopy after NaOH–EDTA extraction revealed that P was predominantly in the inorganic pool in all three sludge samples, with the highest proportion (of the total extracted P) as inorganic P in the anaerobically digested liquid sludge. After sludge incorporation, P was immobilised to organic species. The majority of organic P was in monoester-P forms, while the remainder of organic P (diester P and phosphonate P) was more susceptible to transformations through time and showed variation with sludge type. These results show that application of sewage sludge at rates as low as 4t ha−1 can have a significant nutritional benefit to ryegrass over an initial 35-day growth and subsequent 35-day re-growth periods. Differences in P transformation, and hence nutritional benefit, between sludge types were evident throughout the experiment. Thus, differences in sludge treatment process alter the edaphic mineralisation characteristics of biosolids derived from the same source material.

Nitrogen dynamics under Lolium perenne after a single application of different sewage sludge types from the same treatment stream

Three sludge types from the same treatment stream (undigested liquid, anaerobically digested liquid and dewatered, anaerobically digested cake) were used in a field based tub study. Amendments (4, 8, and 16 Mg dry solid (ds)ha(-1)) were incorporated into the upper 15 cm of a sandy loam soil prior to sowing with rye-grass (Lolium perenne L.). Nitrogen transformations in the soil were determined for the 80 d period following incorporation. Nitrogen uptake and crop yield were measured in the cut sward 35 and 70 d after sowing. The study showed that application of sewage sludge at rates as low as 4 Mgha(-1) can have a nutritional benefit to rye-grass over the two harvests. Differences in N transformation, and hence crop nutritional benefit, between sludge types were evident throughout the experiment. In particular, the dewatering process changed the mineral N characteristics of the anaerobically digested sludge, which, when not dewatered, outperformed the other sludges in terms of yield and mineralisation rate at both harvests. The dewatered sludge produced the lowest yield of rye-grass. The undigested liquid sludge had the lowest foliar N and soil NO(3)-N concentrations, possibly immobilised as the large oxidisable C component of this sludge was metabolised by the microbial biomass. Correlation data support the concept of preferential uptake of NH(4)-N over NO(3)-N in Lolium perenne. Results are discussed in the context of managing sludge type and application for a plant nutrient source and NO(3)-N release.

Big social data and political sentiment: the tweet stream during the UK General Election 2015 campaign

The General Election for the 56th United Kingdom Parliament was held on 7 May 2015. Tweets related to UK politics, not only those with the specific hashtag ”#GE2015”, have been collected in the period between March 1 and May 31, 2015. The resulting dataset contains over 28 million tweets for a total of 118 GB in uncompressed format or 15 GB in compressed format. This study describes the method that was used to collect the tweets and presents some analysis, including a political sentiment index, and outlines interesting research directions on Big Social Data based on Twitter microblogging.

Differentiation of neotropical ecosystems by modern soil phytolith assemblages and its implications for palaeoenvironmental and archaeological reconstructions II: Southwestern Amazonian forests

Accurate archaeological and palaeoenvironmental reconstructions using phytoliths relies on the study of modern reference material. In eastern Acre, Brazil, we examined whether the five most common forest types present today were able to be differentiated by their soil phytolith assemblages, and thus provide analogues with which to compare palaeoecological assemblages from pre-Columbian earthwork sites in the region. Surface soils and vegetation from dense humid evergreen forest, dense humid evergreen forest with high palm abundance, palm forest, bamboo forest and fluvial forest were sampled and their phytoliths analysed. Relative phytolith frequencies were statistically compared using Principal Components Analyses (PCAs). We found the major differences in species composition to be well-represented by the phytolith assemblages as all forest types, apart from the two sub-types of dense humid evergreen forest, could be differentiated. Larger phytoliths from the sand fraction were found to be more ecologically diagnostic than those from the silt fraction. The surface soil phytolith assemblages we analysed can therefore be used as analogues to improve the accuracy of archaeological and palaeoecological reconstructions in the region.

SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.