Abstract.To assess the history of greenhouse gas emissions and individual countries' contributions to emissions and climate change, detailed historical data are needed. We combine several published datasets to create a comprehensive set of emissions pathways for each country and Kyoto gas, covering the years 1850 to 2014 with yearly values, for all UNFCCC member states and most non-UNFCCC territories. The sectoral resolution is that of the main IPCC 1996 categories. Additional time series of CO 2 are available for energy and industry subsectors. Country-resolved data are combined from different sources and supplemented using year-to-year growth rates from regionally resolved sources and numerical extrapolations to complete the dataset. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and simulations of agricultural land. In this paper, we discuss the data sources and methods used and present the resulting dataset, including its limitations and uncertainties. The dataset is available from doi:10.5880/PIK.2016.003 and can be viewed on the website accompanying this paper

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Abstract. To assess the history of greenhouse gas emissions and individual countries' contributions to emissions and climate change, detailed historical data is needed. We combine several published datasets to create a comprehensive set of emission pathways of each country and Kyoto gas covering the years 1850 to 2014 for all UNFCCC member states as well as most non-UNFCCC territories. The sectoral resolution is that of the main IPCC 1996 categories. Additional subsectors are available for time series of CO2 from energy and industry. Country resolved data is combined from different sources and supplemented using growth rates from region resolved sources and numerical extrapolations to complete the dataset. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and simulations of agricultural land. In this paper, we discuss the data sources and methods used and present the resulting dataset including its limitations and uncertainties. The dataset is available from http://doi.org/10.5880/PIK.2016.003 and can be viewed on the website accompanying this paper (www.pik-potsdam.de/primap-live/primap-hist/).

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Summary In most biodiversity studies, taxonomic diversity is the measure for the multiplicity of species and is often considered to represent functional diversity. However, trends in taxonomic diversity and functional diversity may differ, for example, when many functionally similar but taxonomically different species co‐occur in a community. The differences between these diversity measures are of particular interest in diversity research for understanding diversity patterns and their underlying mechanisms. We analysed a temporally highly resolved 20‐year time series of lake phytoplankton to determine whether taxonomic diversity and functional diversity exhibit similar or contrasting seasonal patterns. We also calculated the functional mean of the community in n‐dimensional trait space for each sampling day to gain further insights into the seasonal dynamics of the functional properties of the community. We found an overall weak positive relationship between taxonomic diversity and functional diversity with a distinct seasonal pattern. The two diversity measures showed synchronous behaviour from early spring to mid‐summer and a more complex and diverging relationship from autumn to late winter. The functional mean of the community exhibited a recurrent annual pattern with the most prominent changes before and after the clear‐water phase. From late autumn to winter, the functional mean of the community and functional diversity were relatively constant while taxonomic diversity declined, suggesting competitive exclusion during this period. A further decline in taxonomic diversity concomitant with increasing functional diversity in late winter to early spring is seen as a result of niche diversification together with competitive exclusion. Under these conditions, several different sets of traits are suitable to thrive, but within one set of functional traits only one, or very few, morphotypes can persist. Taxonomic diversity alone is a weak descriptor of trait diversity in phytoplankton. However, the combined analysis of taxonomic diversity and functional diversity, along with the functional mean of the community, allows for deeper insights into temporal patterns of community assembly and niche diversification.