PalaeoDrivers seeks to capitalise on the recent development of methods to test the statistical significance of quantitative palaeoecological reconstructions. This NFR-funded project is led by Dr Richard Telford and employs Mathias Trachsel as a post-doc.
The main aims are to further develop and test the methods illustrated in Telford and Birks (2011); to use these methods to generate urgently needed improved guidelines for the optimal design of modern calibration-sets; and to apply these methods to existing palaeoecological data-sets to address long-standing problems with these novel approaches, for example, to determine which environmental variables are the most important drivers of late Quaternary biotic change.
Past environmental conditions can be reconstructed from fossil pollen and other microfossil assemblages using the modern relationship between the species and the environment. Such palaeoenvironmental reconstructions have been important evidence for several policy-relevant scientific debates. For example, diatom-based reconstructions of lake-pH contributed to the acid rain debate by showing that Norwegian lakes had acidified recently and simultaneously with increased atmospheric pollution, and reconstructions of past climate, using pollen preserved in lake sediments to infer air temperatures, or foraminifera in ocean sediments to infer sea surface temperatures, have helped understand natural climatic variability. Despite the widespread use of palaeoenvironmental reconstructions, it has been difficult to assess how robust they are, potentially allowing weak or erroneous reconstructions to be published. Recent work by Telford and Birks (2011) developed a method to determine the statistical significance of palaeoenvironmental reconstructions by testing if the reconstruction explains more of the variance in the fossil data than most reconstructions of random environmental variables.
This project will extend these significance tests. The key challenge will be to develop appropriate null models. With our novel methods, we will be able to test which environmental variable, or combination of variables, best explains biotic changes. For example, using pollen data, we will determine whether precipitation or temperature changes best explain vegetation dynamics on the Tibetan Plateau or in North America during the Holocene. We will also generate guidelines for the design of modern species-environment calibration sets to maximise the likelihood of obtaining statistically significant reconstructions rather than the current practice of attempting to maximise cross-validation performance. This project will help make palaeoecology a more quantitative, rigorous and robust science.
The products PalaeoDrivers will generate are of interest to palaeoecologists who use transfer functions to reconstruct natural or human-induced environmental change, and to palaeoecologists who are interested in the impact of environmental change on biota. The first audience urgently needs new advice on how to optimally design calibration-sets for robust reconstructions. Presentations relevant to both audiences will be given at international conferences, including the International Paleolimnology Symposium (2012), INQUA (2015), and EGU (annual). The results of the PalaeoDrivers project will be communicated to colleagues at Bjerknes Centre for Climate Research through seminars to help influence the chapter on Information from Paleoclimate Archives in the forthcoming IPCC 5th Assessment Report on Information from Paleoclimate Archives. R code for the methods developed in this project will be added to the palaeoSig package (Telford 2011) to make the methods available for the palaeoecological community to use. Care will be taken to ensure that the documentation is adequate to use the methods without assistance. It is critical to the long-term success of PalaeoDrivers that the methods developed by the project are adopted by the community to improve quantitative methods. To help achieve this aim, PalaeoDrivers will support a training workshop for early-stage scientists in 2013.
The project will directly benefit from exposure to different data sets that may present novel issues, and from the researchers using the methods and potentially revealing deficiencies in either the R code or the documentation. Young researchers will benefit from training them in the methods, giving them early exposure to them.