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The Neolithic transition has led to marked increases in census population sizes across the world, as recorded by a rich archaeological record. However, previous attempts to detect such changes using genetic markers, especially mitochondrial DNA (mtDNA), have mostly been unsuccessful. We use complete mtDNA genomes from over 1700 individuals, from the 1000 Genomes Project Phase 3, to explore changes in populations sizes in five populations for each of four major geographical regions, using a sophisticated coalescent-based Bayesian method (extended Bayesian skyline plots) and mutation rates calibrated with ancient DNA. Despite the power and sophistication of our analysis, we fail to find size changes that correspond to the Neolithic transitions of the study populations. However, we do detect a number of size changes, which tend to be replicated in most populations within each region. These changes are mostly much older than the Neolithic transition and could reflect either population expansion or changes in population structure. Given the amount of migration and population mixing that occurred after these ancient signals were generated, we caution that modern populations will often carry ghost signals of demographic events that occurred far away from their current location.

During the Quaternary, large climate oscillations impacted the distribution and demography of species globally. Two approaches have played a major role in reconstructing changes through time: Bayesian Skyline Plots (BSPs), which reconstruct population fluctuations based on genetic data, and Species Distribution Models (SDMs), which allow us to back‐cast the range occupied by a species based on its climatic preferences. In this paper, we contrast these two approaches by applying them to a large data set of 102 Holarctic bird species, for which both mitochondrial DNA sequences and distribution maps are available, to reconstruct their dynamics since the Last Glacial Maximum (LGM). Most species experienced an increase in effective population size (Ne, as estimated by BSPs) as well as an increase in geographical range (as reconstructed by SDMs) since the LGM; however, we found no correlation between the magnitude of changes in Ne and range size. The only clear signal we could detect was a later and greater increase in Ne for wetland birds compared to species that live in other habitats, a probable consequence of a delayed and more extensive increase in the extent of this habitat type after the LGM. The lack of correlation between SDM and BSP reconstructions could not be reconciled even when range shifts were considered. We suggest that this pattern might be linked to changes in population densities, which can be independent of range changes, and caution that interpreting either SDMs or BSPs independently is problematic and potentially misleading.

During the Quaternary, large climate oscillations impacted the distribution and demography of species globally. Two approaches have played a major role in reconstructing changes through time: Bayesian Skyline Plots (BSPs), which reconstruct population fluctuations based on genetic data, and Species Distribution Models (SDMs), which allow us to back-cast the range occupied by a species based on its climatic preferences. In this paper, we contrast these two approaches by applying them to a large data set of 102 Holarctic bird species, for which both mitochondrial DNA sequences and distribution maps are available, to reconstruct their dynamics since the Last Glacial Maximum (LGM). Most species experienced an increase in effective population size (Ne , as estimated by BSPs) as well as an increase in geographical range (as reconstructed by SDMs) since the LGM; however, we found no correlation between the magnitude of changes in Ne and range size. The only clear signal we could detect was a later and greater increase in Ne for wetland birds compared to species that live in other habitats, a probable consequence of a delayed and more extensive increase in the extent of this habitat type after the LGM. The lack of correlation between SDM and BSP reconstructions could not be reconciled even when range shifts were considered. We suggest that this pattern might be linked to changes in population densities, which can be independent of range changes, and caution that interpreting either SDMs or BSPs independently is problematic and potentially misleading.

During the Quaternary, large climate oscillations impacted the distribution and demography of species globally. Two approaches have played a major role in reconstructing changes through time: Bayesian Skyline Plots (BSPs), which reconstruct population fluctuations based on genetic data, and Species Distribution Models (SDMs), which allow us to back‐cast the range occupied by a species based on its climatic preferences. In this paper, we contrast these two approaches by applying them to a large data set of 102 Holarctic bird species, for which both mitochondrial DNA sequences and distribution maps are available, to reconstruct their dynamics since the Last Glacial Maximum (LGM). Most species experienced an increase in effective population size (Ne, as estimated by BSPs) as well as an increase in geographical range (as reconstructed by SDMs) since the LGM; however, we found no correlation between the magnitude of changes in Ne and range size. The only clear signal we could detect was a later and greater increase in Ne for wetland birds compared to species that live in other habitats, a probable consequence of a delayed and more extensive increase in the extent of this habitat type after the LGM. The lack of correlation between SDM and BSP reconstructions could not be reconciled even when range shifts were considered. We suggest that this pattern might be linked to changes in population densities, which can be independent of range changes, and caution that interpreting either SDMs or BSPs independently is problematic and potentially misleading.

Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.