Building the niche through time: using 13,000 years of data to predict the effects of climate change on three tree species in Europe

Aim  Species distribution models (SDMs) based on current species ranges underestimate the potential distribution when projected in time and/or space. A multi-temporal model calibration approach has been suggested as an alternative, and we evaluate this using 13,000 years of data. Location  Europe. Methods  We used fossil-based records of presence for Picea abies, Abies alba and Fagus sylvatica and six climatic variables for the period 13,000 to 1000 yr bp. To measure the contribution of each 1000-year time step to the total niche of each species (the niche measured by pooling all the data), we employed a principal components analysis (PCA) calibrated with data over the entire range of possible climates. Then we projected both the total niche and the partial niches from single time frames into the PCA space, and tested if the partial niches were more similar to the total niche than random. Using an ensemble forecasting approach, we calibrated SDMs for each time frame and for the pooled database. We projected each model to current climate and evaluated the results against current pollen data. We also projected all models into the future. Results  Niche similarity between the partial and the total-SDMs was almost always statistically significant and increased through time. SDMs calibrated from single time frames gave different results when projected to current climate, providing evidence of a change in the species realized niches through time. Moreover, they predicted limited climate suitability when compared with the total-SDMs. The same results were obtained when projected to future climates. Main conclusions  The realized climatic niche of species differed for current and future climates when SDMs were calibrated considering different past climates. Building the niche as an ensemble through time represents a way forward to a better understanding of a species' range and its ecology in a changing climate.

Forecasting US output growth with non-linear models in the presence of data uncertainty

We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model's forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.

Forecasting with vector autoregressive models of data vintages: US output growth and inflation

Vintage-based vector autoregressive models of a single macroeconomic variable are shown to be a useful vehicle for obtaining forecasts of different maturities of future and past observations, including estimates of post-revision values. The forecasting performance of models which include information on annual revisions is superior to that of models which only include the first two data releases. However, the empirical results indicate that a model which reflects the seasonal nature of data releases more closely does not offer much improvement over an unrestricted vintage-based model which includes three rounds of annual revisions.

Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions

We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

The role of statistical learning in the acquisition of motion event construal in a second language

Learning to talk about motion in a second language is very difficult because it involves restructuring deeply entrenched patterns from the first language (Slobin 1996). In this paper we argue that statistical learning (Saffran et al. 1997) can explain why L2 learners are only partially successful in restructuring their second language grammars. We explore to what extent L2 learners make use of two mechanisms of statistical learning, entrenchment and pre-emption (Boyd and Goldberg 2011) to acquire target-like expressions of motion and retreat from overgeneralisation in this domain. Paying attention to the frequency of existing patterns in the input can help learners to adjust the frequency with which they use path and manner verbs in French but is insufficient to acquire the boundary crossing constraint (Slobin and Hoiting 1994) and learn what not to say. We also look at the role of language proficiency and exposure to French in explaining the findings.

Increased dietary α-linolenic acid has sex-specific effects upon eicosapentaenoic acid status in humans: re-examination of data from a randomised, placebo-controlled, parallel study

Background: There is a metabolic pathway by which mammals can convert the omega-3 (n-3) essential fatty acid α-linolenic acid (ALA) into longer-chain n-3 polyunsaturated fatty acids (LC n-3 PUFA) including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). As far as we know there are currently no studies that have specifically examined sex differences in the LC n-3 PUFA response to increased dietary ALA intake in humans, although acute studies with isotope-labelled ALA identified that women have a significantly greater capacity to synthesise EPA and DHA from ALA compared to men. Findings: Available data from a placebo-controlled, randomised study were re-examined to identify whether there are sex differences in the LC n-3 PUFA response to increased dietary ALA intake in humans. There was a significant difference between sexes in the response to increased dietary ALA, with women having a significantly greater increase in the EPA content of plasma phospholipids (mean +2.0% of total fatty acids) after six months of an ALA-rich diet compared to men (mean +0.7%, P = 0.039). Age and BMI were identified as predictors of response to dietary ALA among women. Conclusions: Women show a greater increase in circulating EPA than men during increased dietary ALA consumption. Further understanding of individual variation in the response to dietary ALA could inform nutrition advice, with recommendations being specifically tailored according to habitual diet, sex, age and BMI.

Rapid preconditioning of data for accelerating convex hull algorithms

Given a dataset of two-dimensional points in the plane with integer coordinates, the method proposed reduces a set of n points down to a set of s points s ≤ n, such that the convex hull on the set of s points is the same as the convex hull of the original set of n points. The method is O(n). It helps any convex hull algorithm run faster. The empirical analysis of a practical case shows a percentage reduction in points of over 98%, that is reflected as a faster computation with a speedup factor of at least 4.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Research data management and openness: the role of data sharing in developing institutional policies and practices

Purpose: To investigate the relationship between research data management (RDM) and data sharing in the formulation of RDM policies and development of practices in higher education institutions (HEIs). Design/methodology/approach: Two strands of work were undertaken sequentially: firstly, content analysis of 37 RDM policies from UK HEIs; secondly, two detailed case studies of institutions with different approaches to RDM based on semi-structured interviews with staff involved in the development of RDM policy and services. The data are interpreted using insights from Actor Network Theory. Findings: RDM policy formation and service development has created a complex set of networks within and beyond institutions involving different professional groups with widely varying priorities shaping activities. Data sharing is considered an important activity in the policies and services of HEIs studied, but its prominence can in most cases be attributed to the positions adopted by large research funders. Research limitations/implications: The case studies, as research based on qualitative data, cannot be assumed to be universally applicable but do illustrate a variety of issues and challenges experienced more generally, particularly in the UK. Practical implications: The research may help to inform development of policy and practice in RDM in HEIs and funder organisations. Originality/value: This paper makes an early contribution to the RDM literature on the specific topic of the relationship between RDM policy and services, and openness – a topic which to date has received limited attention.

Do arbuscular mycorrhizas or heterotrophic soil microbes contribute toward plant acquisition of a pulse of mineral phosphate?

Aims: We investigated the role of arbuscular mycorrhizal fungi (AMF) and heterotrophic soil microbes in the uptake of phosphorus (P) by Trifolium subterraneum from a pulse. Methods: Plants were grown in sterilised pasture field soil with a realistic level of available P. There were five treatments, two of which involved AMF: 1) unsterilised field soil containing a community of AMF and heterotrophic organisms; 2) Scutellospora calospora inoculum (AMF); 3) microbes added as filtrate from the field soil; 4) microbes added as filtrate from the S. calospora inoculum; 5) no additions, i.e. sterilised field soil. After 11 weeks, plants were harvested: 1 day before (day 0), 1 day after (day 2) and 7 days after (day 8) the pulse of P (10 mg kg−1). Results: There was no difference among treatments in shoot and root dry weight, which increased from day 0 to day 8. At day 0, shoots and roots of plants in the colonised treatments had higher P and lower Mn concentrations. After the pulse, the rate of increase in P concentration in the shoots was slower for the colonised plants, and the root Mn concentration declined by up to 50 % by day 2. Conclusions: Plants colonised by AMF had a lower rate of increase in shoot P concentration after a pulse, perhaps because intraradical hyphae accumulated P and thus reduced its transport to the shoots.