Gene sequences of the lichen Cetraria aculeata

Lichens are symbioses between fungi (mycobionts) and photoautotrophic green algae or cyanobacteria (photobionts). Many lichens occupy large distributional ranges covering several climatic zones. So far, little is known about the large-scale phylogeography of lichen photobionts and their role in shaping the distributional ranges of lichens. We studied south polar, temperate and north polar populations of the widely distributed fruticose lichen Cetraria aculeata. Based on the DNA sequences from three loci for each symbiont, we compared the genetic structure of mycobionts and photobionts. Phylogenetic reconstructions and Bayesian clustering methods divided the mycobiont and photobiont data sets into three groups. An AMOVA shows that the genetic variance of the photobiont is best explained by differentiation between temperate and polar regions and that of the mycobiont by an interaction of climatic and geographical factors. By partialling out the relative contribution of climate, geography and codispersal, we found that the most relevant factors shaping the genetic structure of the photobiont are climate and a history of codispersal. Mycobionts in the temperate region are consistently associated with a specific photobiont lineage. We therefore conclude that a photobiont switch in the past enabled C. aculeata to colonize temperate as well as polar habitats. Rare photobiont switches may increase the geographical range and ecological niche of lichen mycobionts by associating them with locally adapted photobionts in climatically different regions and, together with isolation by distance, may lead to genetic isolation between populations and thus drive the evolution of lichens.

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.