BackgroundTo assess population persistence of species living in heterogeneous landscapes, the effects of habitat on reproduction and survival have to be investigated.Methodology/Principal FindingsWe used a matrix population model to estimate habitat-specific population growth rates for a population of northern wheatears Oenanthe oenanthe breeding in farmland consisting of a mosaic of distinct habitat (land use) types. Based on extensive long-term data on reproduction and survival, habitats characterised by tall field layers (spring- and autumn-sown crop fields, ungrazed grasslands) displayed negative stochastic population growth rates (log λs: −0.332, −0.429, −0.168, respectively), that were markedly lower than growth rates of habitats characterised by permanently short field layers (pastures grazed by cattle or horses, and farmyards, log λs: −0.056, +0.081, −0.059). Although habitats differed with respect to reproductive performance, differences in habitat-specific population growth were largely due to differences in adult and first-year survival rates, as shown by a life table response experiment (LTRE).Conclusions/SignificanceOur results show that estimation of survival rates is important for realistic assessments of habitat quality. Results also indicate that grazed grasslands and farmyards may act as source habitats, whereas crop fields and ungrazed grasslands with tall field layers may act as sink habitats. We suggest that the strong decline of northern wheatears in Swedish farmland may be linked to the corresponding observed loss of high quality breeding habitat, i.e. grazed semi-natural grasslands.

BioMAX is the first macromolecular crystallography beamline at the MAX IV Laboratory 3 GeV storage ring, which is the first operational multi-bend achromat storage ring. Due to the low-emittance storage ring, BioMAX has a parallel, high-intensity X-ray beam, even when focused down to 20 µm × 5 µm using the bendable focusing mirrors. The beam is tunable in the energy range 5–25 keV using the in-vacuum undulator and the horizontally deflecting double-crystal monochromator. BioMAX is equipped with an MD3 diffractometer, an ISARA high-capacity sample changer and an EIGER 16M hybrid pixel detector. Data collection at BioMAX is controlled using the newly developed MXCuBE3 graphical user interface, and sample tracking is handled by ISPyB. The computing infrastructure includes data storage and processing both at MAX IV and the Lund University supercomputing center LUNARC. With state-of-the-art instrumentation, a high degree of automation, a user-friendly control system interface and remote operation, BioMAX provides an excellent facility for most macromolecular crystallography experiments. Serial crystallography using either a high-viscosity extruder injector or the MD3 as a fixed-target scanner is already implemented. The serial crystallography activities at MAX IV Laboratory will be further developed at the microfocus beamline MicroMAX, when it comes into operation in 2022. MicroMAX will have a 1 µm × 1 µm beam focus and a flux up to 1015 photons s−1 with main applications in serial crystallography, room-temperature structure determinations and time-resolved experiments.

Long term data to estimate population trends among species are generally lacking. However, Natural History Collections (NHCs) can provide such information, but may suffer from biases due to varying sampling effort. To analyze population trends and range-abundance dynamics of Swedish longhorn beetles (Coleoptera: Cerambycidae), we used collections of 108 species stretching over 100 years. We controlled for varying sampling effort by using the total number of database records as a reference for non-red-listed species. Because the general frequency of red-listed species increased over time, a separate estimate of sampling effort was used for that group. We observed large interspecific variation in population changes, from declines of 60% to several hundred percent increases. Most species showed stable or increasing ranges, whereas few seemed to decline in range. Among increasing species, rare species seemed to expand their range more than common species did, but this pattern was not observed in declining species. Historically, rare species did not seem to be at larger risk of local extinction, and population declines were mostly due to lower population density and not loss of sub-populations. We also evaluated the species' declines under IUCN red-list criterion A, and four currently not red-listed species meet the suggested threshold for Near Threatened (NT). The results also suggested that species' declines may be overlooked if estimated only from changes in species range.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Online enhancement: appendix. The University of Chicago Press andabstract: Demographic stochasticity is important in determining extinction risks of small populations, but it is largely unknown how its effect depends on the life histories of species. We modeled effects of demographic stochasticity on extinction risk in a broad range of generalized life histories, using matrix models and branching processes. Extinction risks of life histories varied greatly in their sensitivity to demographic stochasticity. Comparing life histories, extinction risk generally increased with increasing fecundity and decreased with higher ages of maturation. Effects of adult survival depended on age of maturation. At lower ages of maturation, extinction risk peaked at intermediate levels of adult survival, but it increased along with adult survival at higher ages of maturation. These differences were largely explained by differences in sensitivities of population growth to perturbations of life-history traits. Juvenile survival rate contributed most to total demographic variance in the majority of life histories. Our general results confirmed earlier findings, suggesting that empirical patterns can be explained by a relatively simple model. Thus, basic life-history information can be used to assign life-history-specific sensitivity to demographic stochasticity. This is of great value when assessing the vulnerability of small populations.