Spatial autocorrelation in the response of soft-bottom marine benthos to gas extraction activities: The case of amphipods in the Ionian Sea

The spatial distributions of marine fauna and of pollution are both highly structured, and thus the resulting high levels of autocorrelation may invalidate conclusions based on classical statistical approaches. Here we analyse the close correlation observed between proxies for the disturbance associated with gas extraction activities and amphipod distribution patterns around four hydrocarbon platforms. We quantified the amount of variation independently accounted for by natural environmental variables, proxies for the disturbance caused by platforms, and spatial autocorrelation. This allowed us to demonstrate how each of these three factors significantly affects the community structure of amphipods. Sophisticated statistical techniques are required when taking into account spatial autocorrelation: nevertheless our data demonstrate that this approach not only enables the formulation of robust statistical inferences but also provides a much deeper understanding of the subtle interactions between human disturbance and natural factors affecting the structure of marine invertebrates communities. (C) 2012 Elsevier Ltd. All rights reserved.

Role of Predalors, Habitat Attributes, and Spatial Autocorrelation on the Distribution of Eggs in the Northern Spectacled Salamander (Salamandrina perspicillata)

We predicted that the probability of egg occurrence of salamander Salamandrina perspicillata depended on stream features and predation by native crayfish Austropotamobius fulcisianus and the introduced trout Salmo trutta. We assessed the presence of S. perspicillata at 54 sites within a natural reserve of southern Tuscany, Italy. Generalized linear models with binomial errors were constructed using egg presence/absence and altitude, stream mean size and slope, electrical conductivity, water pH and temperature, and a predation factor, defined according to the presence/absence of crayfish and trout. Some competing models also included an autocovariate term, which estimated how much the response variable at any one sampling point reflected response values at surrounding points. The resulting models were compared using Akaike's information criterion. Model selection led to a subset of 14 models with Delta AIC(c) <7 (i.e., models ranging from substantial support to considerably less support), and all but one of these included an effect of predation. Models with the autocovariate term had considerably more support than those without the term. According to multimodel inference, the presence of trout and crayfish reduced the probability of egg occurrence from a mean level of 0.90 (SE limits: 0.98-0.55) to 0.12 (SE limits: 0.34-0.04). The presence of crayfish alone had no detectable effects (SE limits: 0.86-0.39). The results suggest that introduced trout have a detrimental effect on the reproductive output of S. perspicillata and confirm the fundamental importance of distinguishing the roles of endogenous and exogenous forces that act on population distribution.

Spatial patterns and autocorrelation in the response of microarthropods to soil pollutants: The example of oribatid mites in an abandoned mining and smelting area

Although exogenous factors such as pollutants can act on endogenous drivers (e.g. dispersion) of populations and create spatially autocorrelated distributions, most statistical techniques assume independence of error terms. As there are no studies on metal soil pollutants and microarthropods that explicitly analyse this key issue, we completed a field study of the correlation between Oribatida and metal concentrations in litter, organic matter and soil in an attempt to account for spatial patterns of both metals and mites. The 50-m wide study area had homogenous macroscopic features, steep Pb and Cu gradients and high levels of Zn and Cd. Spatial models failed to detect metal-oribatid relationships because the observed latitudinal and longitudinal gradients in oribatid assemblages were independent of the collinear gradients in the concentration of metals. It is therefore hypothesised that other spatially variable factors (e.g. fungi, reduced macrofauna) affect oribatid assemblages, which may be influenced by metals only indirectly. (C) 2009 Elsevier Ltd. All rights reserved.

Statistics sunspots and settlement: Influences on sum of probability curves

Large data sets of radiocarbon dates are becoming a more common feature of archaeological research. The sheer numbers of radiocarbon dates produced, however, raise issues of representation and interpretation. This paper presents a methodology which both reduces the visible impact of dating fluctuations, but also takes into consideration the influence of the underlying radiocarbon calibration curve. By doing so, it may be possible to distinguish between periods of human activity in early medieval Ireland and the statistical tails produced by radiocarbon calibration.

Spatial statistics to estimate peat thickness using airborne radiometric data

Soil carbon stores are a major component of the annual returns required by EU governments to the Intergovernmental Panel on Climate Change. Peat has a high proportion of soil carbon due to the relatively high carbon density of peat and organic-rich soils. For this reason it has become increasingly important to measure and model soil carbon stores and changes in peat stocks to facilitate the management of carbon changes over time. The approach investigated in this research evaluates the use of airborne geophysical (radiometric) data to estimate peat thickness using the attenuation of bedrock geology radioactivity by superficial peat cover. Remotely sensed radiometric data are validated with ground peat depth measurements combined with non-invasive geophysical surveys. Two field-based case studies exemplify and validate the results. Variography and kriging are used to predict peat thickness from point measurements of peat depth and airborne radiometric data and provide an estimate of uncertainty in the predictions. Cokriging, by assessing the degree of spatial correlation between recent remote sensed geophysical monitoring and previous peat depth models, is used to examine changes in peat stocks over time. The significance of the coregionalisation is that the spatial cross correlation between the remote and ground based data can be used to update the model of peat depth. The result is that by integrating remotely sensed data with ground geophysics, the need is reduced for extensive ground-based monitoring and invasive peat depth measurements. The overall goal is to provide robust estimates of peat thickness to improve estimates of carbon stocks. The implications from the research have a broader significance that promotes a reduction in the need for damaging onsite peat thickness measurement and an increase in the use of remote sensed data for carbon stock estimations.

Dyslexia in higher education: Implications for maths anxiety, statistics anxiety and psychological well-being

This study examined levels of mathematics and statistics anxiety, as well as general mental health amongst undergraduate students with dyslexia (n = 28) and those without dyslexia (n = 71). Students with dyslexia had higher levels of mathematics anxiety relative to those without dyslexia, while statistics anxiety and general mental health were comparable for both reading ability groups. In terms of coping strategies, undergraduates with dyslexia tended to use planning-based strategies and seek instrumental support more frequently than those without dyslexia. Higher mathematics anxiety was associated with having a dyslexia diagnosis, as well as greater levels of worrying, denial, seeking instrumental support and less use of the positive reinterpretation coping strategy. By contrast, statistics anxiety was not predicted by dyslexia diagnosis, but was instead predicted by overall worrying and the use of denial and emotion focused coping strategies. The results suggest that disability practitioners should be aware that university students with dyslexia are at risk of high mathematics anxiety. Additionally, effective anxiety reduction strategies such as positive reframing and thought challenging would form a useful addition to the support package delivered to many students with dyslexia.

Integrating Multiple Point Statistics with Aerial Geophysical Data to assist Groundwater Flow Models

The process of accounting for heterogeneity has made significant advances in statistical research, primarily in the framework of stochastic analysis and the development of multiple-point statistics (MPS). Among MPS techniques, the direct sampling (DS) method is tested to determine its ability to delineate heterogeneity from aerial magnetics data in a regional sandstone aquifer intruded by low-permeability volcanic dykes in Northern Ireland, UK. The use of two two-dimensional bivariate training images aids in creating spatial probability distributions of heterogeneities of hydrogeological interest, despite relatively ‘noisy’ magnetics data (i.e. including hydrogeologically irrelevant urban noise and regional geologic effects). These distributions are incorporated into a hierarchy system where previously published density function and upscaling methods are applied to derive regional distributions of equivalent hydraulic conductivity tensor K. Several K models, as determined by several stochastic realisations of MPS dyke locations, are computed within groundwater flow models and evaluated by comparing modelled heads with field observations. Results show a significant improvement in model calibration when compared to a simplistic homogeneous and isotropic aquifer model that does not account for the dyke occurrence evidenced by airborne magnetic data. The best model is obtained when normal and reverse polarity dykes are computed separately within MPS simulations and when a probability threshold of 0.7 is applied. The presented stochastic approach also provides improvement when compared to a previously published deterministic anisotropic model based on the unprocessed (i.e. noisy) airborne magnetics. This demonstrates the potential of coupling MPS to airborne geophysical data for regional groundwater modelling.

Detecting the work statistics through Ramsey-like interferometry

Out-of-equilibrium statistical mechanics is attracting considerable interest due to the recent advances in the control and manipulations of systems at the quantum level. Recently, an interferometric scheme for the detection of the characteristic function of the work distribution following a time-dependent process has been proposed [L. Mazzola et al., Phys. Rev. Lett. 110 (2013) 230602]. There, it was demonstrated that the work statistics of a quantum system undergoing a process can be reconstructed by effectively mapping the characteristic function of work on the state of an ancillary qubit. Here, we expand that work in two important directions. We first apply the protocol to an interesting specific physical example consisting of a superconducting qubit dispersively coupled to the field of a microwave resonator, thus enlarging the class of situations for which our scheme would be key in the task highlighted above. We then account for the interaction of the system with an additional one (which might embody an environment), and generalize the protocol accordingly.

Evaluating the use of testate amoebae for palaeohydrological reconstruction in permafrost peatlands

The melting of high-latitude permafrost peatlands is a major concern due to a potential positive feedback on global climate change. We examine the ecology of testate amoebae in permafrost peatlands, based on sites in Sweden (~ 200 km north of the Arctic Circle). Multivariate statistical analysis confirms that water-table depth and moisture content are the dominant controls on the distribution of testate amoebae, corroborating the results from studies in mid-latitude peatlands. We present a new testate amoeba-based water table transfer function and thoroughly test it for the effects of spatial autocorrelation, clustered sampling design and uneven sampling gradients. We find that the transfer function has good predictive power; the best-performing model is based on tolerance-downweighted weighted averaging with inverse deshrinking (performance statistics with leave-one-out cross validation: R2 = 0.87, RMSEP = 5.25 cm). The new transfer function was applied to a short core from Stordalen mire, and reveals a major shift in peatland ecohydrology coincident with the onset of the Little Ice Age (c. AD 1400). We also applied the model to an independent contemporary dataset from Stordalen and find that it outperforms predictions based on other published transfer functions. The new transfer function will enable palaeohydrological reconstruction from permafrost peatlands in Northern Europe, thereby permitting greatly improved understanding of the long-term ecohydrological dynamics of these important carbon stores as well as their responses to recent climate change.