274 research outputs found

    Is more data always better? A simulation study of benefits and limitations of integrated distribution models

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    Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence‐only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence‐only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources

    Ratification vote on taxonomic proposals to the International Committee on Taxonomy of Viruses (2016)

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    This article lists the changes to virus taxonomy approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in April 2016. Changes to virus taxonomy (the Universal Scheme of Virus Classification of the International Committee on Taxonomy of Viruses [ICTV]) now take place annually and are the result of a multi-stage process. In accordance with the ICTV Statutes (http://​www.​ictvonline.​org/​statutes.​asp), proposals submitted to the ICTV Executive Committee (EC) undergo a review process that involves input from the ICTV Study Groups (SGs) and Subcommittees (SCs), other interested virologists, and the EC. After final approval by the EC, proposals are then presented for ratification to the full ICTV membership by publication on an ICTV web site (http://​www.​ictvonline.​org/​) followed by an electronic vote. The latest set of proposals approved by the EC was made available on the ICTV website by January 2016 (https://​talk.​ictvonline.​org/​files/​proposals/​). A list of these proposals was then emailed on 28 March 2016 to the 148 members of ICTV, namely the EC Members, Life Members, ICTV Subcommittee Members (including the SG chairs) and ICTV National Representatives. Members were then requested to vote on whether to ratify the taxonomic proposals (voting closed on 29 April 2016)

    Emissions of carbon tetrachloride from Europe

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    Carbon tetrachloride (CCl4) is a long-lived radiatively active compound with the ability to destroy stratospheric ozone. Due to its inclusion in the Montreal Protocol on Substances that Deplete the Ozone Layer (MP), the last two decades have seen a sharp decrease in its large-scale emissive use with a consequent decline in its atmospheric mole fractions. However, the MP restrictions do not apply to the use of carbon tetrachloride as feedstock for the production of other chemicals, implying the risk of fugitive emissions from the industry sector. The occurrence of such unintended emissions is suggested by a significant discrepancy between global emissions as derived from reported production and feedstock usage (bottom-up emissions), and those based on atmospheric observations (top-down emissions). In order to better constrain the atmospheric budget of carbon tetrachloride, several studies based on a combination of atmospheric observations and inverse modelling have been conducted in recent years in various regions of the world. This study is focused on the European scale and based on long-term high-frequency observations at three European sites, combined with a Bayesian inversion methodology. We estimated that average European emissions for 2006–2014 were 2.2 (± 0.8) Gg yr−1, with an average decreasing trend of 6.9 % per year. Our analysis identified France as the main source of emissions over the whole study period, with an average contribution to total European emissions of approximately 26 %. The inversion was also able to allow the localisation of emission "hot spots" in the domain, with major source areas in southern France, central England (UK) and Benelux (Belgium, the Netherlands, Luxembourg), where most industrial-scale production of basic organic chemicals is located. According to our results, European emissions correspond, on average, to 4.0 % of global emissions for 2006–2012. Together with other regional studies, our results allow a better constraint of the global budget of carbon tetrachloride and a better quantification of the gap between top-down and bottom-up estimates
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