Data quantity is more important than its spatial bias for predictive species distribution modelling

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average number of observations per species), and (3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

Quantifying large-scale ecosystem stability with remote sensing data

To fully understand ecosystem functioning under global change, we need to be able to measure the stability of ecosystem functioning at multiple spatial scales. Although a number of stability components have been established at small spatial scales, there has been little progress in scaling these measures up to the landscape. Remote sensing data holds huge potential for studying processes at landscape scales but requires quantitative measures that are comparable from experimental field data to satellite remote sensing. Here we present a methodology to extract four components of ecosystem functioning stability from satellite-derived time series of Enhanced Vegetation Index (EVI) data. The four stability components are as follows: variability, resistance, recovery time and recovery rate in ecosystem functioning. We apply our method to the island of Ireland to demonstrate the use of remotely sensed data to identify large disturbance events in productivity. Our method uses stability measures that have been established at the field-plot scale to quantify the stability of ecosystem functioning. This makes our method consistent with previous small-scale stability research, whilst dealing with the unique challenges of using remotely sensed data including noise. We encourage the use of remotely-sensed data in assessing the stability of ecosystems at a scale that is relevant to conservation and management practices.

Land cover drives large scale productivity-diversity relationships in Irish vascular plants

The impact of productivity on species diversity is often studied at small spatial scales and without taking additional environmental factors into account. Focusing on small spatial scales removes important regional scale effects, such as the role of land cover heterogeneity. Here, we use a regional spatial scale (10 km square) to establish the relationship between productivity and vascular plant species richness across the island of Ireland that takes into account variation in land cover. We used generalized additive mixed effects models to relate species richness, estimated from biological records, to plant productivity. Productivity was quantified by the satellite-derived enhanced vegetation index. The productivity-diversity relationship was fitted for three land cover types: pasture-dominated, heterogeneous, and non-pasture-dominated landscapes. We find that species richness decreases with increasing productivity, especially at higher productivity levels. This decreasing relationship appears to be driven by pasture-dominated areas. The relationship between species richness and heterogeneity in productivity (both spatial and temporal) varies with land cover. Our results suggest that the impact of pasture on species richness extends beyond field level. The effect of human modified landscapes, therefore, is important to consider when investigating classical ecological relationships, particularly at the wider landscape scale.

Measurement and comparison of individual external doses of high-school students living in Japan, France, Poland and Belarus-the 'D-shuttle' project

Twelve high schools in Japan (of which six are in Fukushima Prefecture), four in France, eight in Poland and two in Belarus cooperated in the measurement and comparison of individual external doses in 2014. In total 216 high-school students and teachers participated in the study. Each participant wore an electronic personal dosimeter 'D-shuttle' for two weeks, and kept a journal of his/her whereabouts and activities. The distributions of annual external doses estimated for each region overlap with each other, demonstrating that the personal external individual doses in locations where residence is currently allowed in Fukushima Prefecture and in Belarus are well within the range of estimated annual doses due to the terrestrial background radiation level of other regions/countries.

A simple calculation for the buildup and decay of radon progeny

A Markov chain in conjunction with an electronic spreadsheet is used to generate the buildup and decay patterns of radon progeny in a procedure involving only multiplication and addition. Markov chains are highly suitable for calculations of this type. Because the Markov matrix may not be readily available, and because some health physicists may not be familiar with this procedure, we give the values of a Markov matrix and demonstrate how it can be used to produce a Markov chain describing the buildup and decay of radon progeny.

Beneficial effects of dopamine combined with intravenous nitroglycerin on hemodynamics in patients with severe left ventricular failure

Hemodynamic effects of dopamine and intravenous nitroglycerin alone, and in combination, were studied in 27 patients with severe left ventricular failure. Dopamine alone increased cardiac index from 1.8 to 2.5 l/min/m2 but also increased wedge pressure from 24 to 30 mm Hg and heart rate from 88 to 101 beats/min. Arterial oxygen saturation fell from 92% to 87% (p less than .001). Nitroglycerin alone had a lesser effect on cardiac index (1.8 to 2.2 l/min/m2) but decreased wedge pressure from 26 to 16 mm Hg and heart rate from 91 to 86 beats/min. Arterial oxygen saturation fell from 91% to 90% (NS). Combined dopamine and nitroglycerin administration resulted in optimal hemodynamics, with cardiac index of 2.9 l/min/m2, wedge pressure of 17 mm Hg, and heart rate of 96 beats/min. Arterial oxygen saturation remained low at 88% in spite of the reduction in left ventricular filling pressure, which probably reflects increased intrapulmonary right-to-left shunting coupled with increased pulmonary blood flow. These results suggest that the combination of dopamine with intravenous nitroglycerin should be considered for patients with severe left ventricular dysfunction who require temporary pharmacologic support.