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This paper investigates the consequences of ignoring the clustered data structure on allometric models. Clustered data, in the form of multiple trees sampled from multiple forest stands is commonly used to develop biomass allometric models. Of 102 reviewed papers published between 2012 and 2016 that reported biomass allometric models, 84 (82%) have used a clustered sampling design. However, in as many as 80% of these, the clustered data structure was ignored, potentially violating the independence assumption in ordinary least squares methods. The consequences of ignoring clustered data structure were empirically validated using two clustered biomass datasets (of 110 and 220 trees, with the cluster size of 5 and 10 trees respectively). We showed that when Intraclass Correlation Coefficient (ICC) was higher than zero, ignoring the clustered data structure returned underestimated standard errors, affecting further the confidence interval and t-test results. The underestimation level depended on ICC (which shows the variance proportion that was caused by the forest stand) and on cluster size (the number of trees sampled from one forest stand). We also showed that using first-order autocorrelation tests, such as the traditional Durbin-Watson statistic, to detect the autocorrelation due to clustered structure could be misleading as the test may show lack of autocorrelation even though ICC is different from zero. In conclusion, when ICC is higher than zero, ignoring the clustered data structure yields over-confident biomass predictions (due to underestimated confidence interval) and/or incorrect research conclusions (due to overestimated evidence against null hypothesis in t-test). Therefore, using a modelling approach that accounts for the hierarchical structure of the data is highly recommended when any form of clustering can be identified, even if the autocorrelation is not significant. [ABSTRACT FROM AUTHOR]

This bibliometric study of a large publication set dealing with research on climate change aims at mapping the relevant literature from a bibliometric perspective and presents a multitude of quantitative data: (1) The growth of the overall publication output as well as (2) of some major subfields, (3) the contributing journals and countries as well as their citation impact, and (4) a title word analysis aiming to illustrate the time evolution and relative importance of specific research topics. The study is based on 222,060 papers (articles and reviews only) published between 1980 and 2014. The total number of papers shows a strong increase with a doubling every 5–6 years. Continental biomass related research is the major subfield, closely followed by climate modeling. Research dealing with adaptation, mitigation, risks, and vulnerability of global warming is comparatively small, but their share of papers increased exponentially since 2005. Research on vulnerability and on adaptation published the largest proportion of very important papers (in terms of citation impact). Climate change research has become an issue also for disciplines beyond the natural sciences. The categories Engineering and Social Sciences show the strongest field-specific relative increase. The Journal of Geophysical Research, the Journal of Climate, the Geophysical Research Letters, and Climatic Change appear at the top positions in terms of the total number of papers published. Research on climate change is quantitatively dominated by the USA, followed by the UK, Germany, and Canada. The citation-based indicators exhibit consistently that the UK has produced the largest proportion of high impact papers compared to the other countries (having published more than 10,000 papers). Also, Switzerland, Denmark and also The Netherlands (with a publication output between around 3,000 and 6,000 papers) perform top—the impact of their contributions is on a high level. The title word analysis shows that the term climate change comes forward with time. Furthermore, the term impact arises and points to research dealing with the various effects of climate change. The discussion of the question of human induced climate change towards a clear fact (for the majority of the scientific community) stimulated research on future pathways for adaptation and mitigation. Finally, the term model and related terms prominently appear independent of time, indicating the high relevance of climate modeling. [ABSTRACT FROM AUTHOR]

Significant progress has been made in the past few years in the development of recommendations, policies, and procedures for creating and promoting citations to data sets, software, and other research infrastructures like computing facilities. Open questions remain, however, about the extent to which referencing practices of authors of scholarly publications are changing in ways desired by these initiatives. This paper uses four focused case studies to evaluate whether research infrastructures are being increasingly identified and referenced in the research literature via persistent citable identifiers. The findings of the case studies show that references to such resources are increasing, but that the patterns of these increases are variable. In addition, the study suggests that citation practices for data sets may change more slowly than citation practices for software and research facilities, due to the inertia of existing practices for referencing the use of data. Similarly, existing practices for acknowledging computing support may slow the adoption of formal citations for computing resources. [ABSTRACT FROM AUTHOR]

Peer-reviewed publications focusing on climate change are growing exponentially with the consequence that the uptake and influence of individual papers varies greatly. Here, we derive metrics of narrativity from psychology and literary theory, and use these metrics to test the hypothesis that more narrative climate change writing is more likely to be influential, using citation frequency as a proxy for influence. From a sample of 732 scientific abstracts drawn from the climate change literature, we find that articles with more narrative abstracts are cited more often. This effect is closely associated with journal identity: higher-impact journals tend to feature more narrative articles, and these articles tend to be cited more often. These results suggest that writing in a more narrative style increases the uptake and influence of articles in climate literature, and perhaps in scientific literature more broadly. [ABSTRACT FROM AUTHOR]

Recent studies in the marketing literature developed a new method for eliciting willingness to pay (WTP) with an open-ended elicitation format: the Range-WTP method. In contrast to the traditional approach of eliciting WTP as a single value (Point-WTP), Range-WTP explicitly allows for preference uncertainty in responses. The aim of this paper is to apply Range-WTP to the domain of contingent valuation and to test for its theoretical validity and robustness in comparison to the Point-WTP. Using data from two novel large-scale surveys on the perception of solar radiation management (SRM), a little-known technique for counteracting climate change, we compare the performance of both methods in the field. In addition to the theoretical validity (i.e. the degree to which WTP values are consistent with theoretical expectations), we analyse the test-retest reliability and stability of our results over time. Our evidence suggests that the Range-WTP method clearly outperforms the Point-WTP method. [ABSTRACT FROM AUTHOR]

Background and Objectives: Changes in relative humidity, along with other meteorological factors, accompany ongoing climate change and play a significant role in weather-related health outcomes, particularly among children. The purpose of this review is to improve our understanding of the relationship between ambient humidity and child health, and to propose directions for future research. Methods: A comprehensive search of electronic databases (PubMed, Medline, Web of Science, ScienceDirect, OvidSP and EBSCO host) and review of reference lists, to supplement relevant studies, were conducted in March 2013. All identified records were selected based on explicit inclusion criteria. We extracted data from the included studies using a pre-designed data extraction form, and then performed a quality assessment. Various heterogeneities precluded a formal quantitative meta-analysis, therefore, evidence was compiled using descriptive summaries. Results: Out of a total of 3797 identified records, 37 papers were selected for inclusion in this review. Among the 37 studies, 35% were focused on allergic diseases and 32% on respiratory system diseases. Quality assessment revealed 78% of the studies had reporting quality scores above 70%, and all findings demonstrated that ambient humidity generally plays an important role in the incidence and prevalence of climate-sensitive diseases among children. Conclusions: With climate change, there is a significant impact of ambient humidity on child health, especially for climate-sensitive infectious diseases, diarrhoeal diseases, respiratory system diseases, and pediatric allergic diseases. However, some inconsistencies in the direction and magnitude of the effects are observed. [ABSTRACT FROM AUTHOR]

Dispersal knowledge is essential for conservation management, and demand is growing. But are we accumulating dispersal knowledge at a pace that can meet the demand? To answer this question we tested for changes in dispersal data collection and use over time. Our systematic review of 655 conservation-related publications compared five topics: climate change, habitat restoration, population viability analysis, land planning (systematic conservation planning) and invasive species. We analysed temporal changes in the: (i) questions asked by dispersal-related research; (ii) methods used to study dispersal; (iii) the quality of dispersal data; (iv) extent that dispersal knowledge is lacking, and; (v) likely consequences of limited dispersal knowledge. Research questions have changed little over time; the same problems examined in the 1990s are still being addressed. The most common methods used to study dispersal were occupancy data, expert opinion and modelling, which often provided indirect, low quality information about dispersal. Although use of genetics for estimating dispersal has increased, new ecological and genetic methods for measuring dispersal are not yet widely adopted. Almost half of the papers identified knowledge gaps related to dispersal. Limited dispersal knowledge often made it impossible to discover ecological processes or compromised conservation outcomes. The quality of dispersal data used in climate change research has increased since the 1990s. In comparison, restoration ecology inadequately addresses large-scale process, whilst the gap between knowledge accumulation and growth in applications may be increasing in land planning. To overcome apparent stagnation in collection and use of dispersal knowledge, researchers need to: (i) improve the quality of available data using new approaches; (ii) understand the complementarities of different methods and; (iii) define the value of different kinds of dispersal information for supporting management decisions. Ambitious, multi-disciplinary research programs studying many species are critical for advancing dispersal research. [ABSTRACT FROM AUTHOR]