A community-based geological reconstruction of Antarctic Ice Sheet deglaciation since the Last Glacial Maximum

A robust understanding of Antarctic Ice Sheet deglacial history since the Last Glacial Maximum is important in order to constrain ice sheet and glacial-isostatic adjustment models, and to explore the forcing mechanisms responsible for ice sheet retreat. Such understanding can be derived from a broad range of geological and glaciological datasets and recent decades have seen an upsurge in such data gathering around the continent and Sub-Antarctic islands. Here, we report a new synthesis of those datasets, based on an accompanying series of reviews of the geological data, organised by sector. We present a series of timeslice maps for 20 ka, 15 ka, 10 ka and 5 ka, including grounding line position and ice sheet thickness changes, along with a clear assessment of levels of confidence. The reconstruction shows that the Antarctic Ice sheet did not everywhere reach the continental shelf edge at its maximum, that initial retreat was asynchronous, and that the spatial pattern of deglaciation was highly variable, particularly on the inner shelf. The deglacial reconstruction is consistent with a moderate overall excess ice volume and with a relatively small Antarctic contribution to meltwater pulse 1a. We discuss key areas of uncertainty both around the continent and by time interval, and we highlight potential priorities for future work. The synthesis is intended to be a resource for the modelling and glacial geological community

Regional climate and vegetation response to orbital forcing within the mid-Pliocene Warm Period: A study using HadCM3

Regional climate and environmental variability in response to orbital forcing during interglacial events within the mid-Piacenzian (Pliocene) Warm Period (mPWP; 3.264–3.025 Ma) has been rarely studied using climate and vegetation models. Here we use climate and vegetation model simulations to predict changes in regional vegetation patterns in response to orbital forcing for four different interglacial events within the mPWP (Marine Isotope Stages (MIS) G17, K1, KM3 and KM5c). The efficacy of model-predicted changes in regional vegetation is assessed by reference to selected high temporal resolution palaeobotanical studies that are theoretically capable of discerning vegetation patterns for the selected interglacial stages. Annual mean surface air temperatures for the studied interglacials are between 0.4 °C to 0.7 °C higher than a comparable Pliocene experiment using modern orbital parameters. Increased spring/summer and reduced autumn/winter insolation in the Northern Hemisphere during MIS G17, K1 and KM3 enhances seasonality in surface air temperature. The two most robust and notable regional responses to this in vegetation cover occur in North America and continental Eurasia, where forests are replaced by more open-types of vegetation (grasslands and shrubland). In these regions our model results appear to be inconsistent with local palaeobotanical data. The orbitally driven changes in seasonal temperature and precipitation lead to a ~ 30% annual reduction in available deep soil moisture (2.0 m from surface), a critical parameter for forest growth, and subsequent reduction in the geographical coverage of forest-type vegetation; a phenomenon not seen in comparable simulations of Pliocene climate and vegetation run with a modern orbital configuration. Our results demonstrate the importance of examining model performance under a range of realistic orbital forcing scenarios within any defined time interval (e.g. mPWP). Additional orbitally resolved records of regional vegetation are needed to further examine the validity of model-predicted regional climate and vegetation responses in greater detail

Molecular characterization of MRSA collected during national surveillance between 2008 and 2019 in the Netherlands

Background.Although the Netherlands is a country with a low endemic level, methicillin-resistant Staphylococcus aureus (MRSA) poses a significant health care problem. Therefore, high coverage national MRSA surveillance has been in place since 1989. To monitor possible changes in the type-distribution and emergence of resistance and virulence, MRSA isolates are molecularly characterized.Methods.All 43,321 isolates from 36,520 persons, collected 2008-2019, were typed by multiple-locus variable number tandem repeats analysis (MLVA) with simultaneous PCR detection of the mecA, mecC and lukF-PV genes, indicative for PVL. Next-generation sequencing data of 4991 isolates from 4798 persons were used for whole genome multi-locus sequence typing (wgMLST) and identification of resistance and virulence genes.Results.We show temporal change in the molecular characteristics of the MRSA population with the proportion of PVL-positive isolates increasing from 15% in 2008-2010 to 25% in 2017-2019. In livestock-associated MRSA obtained from humans, PVL-positivity increases to 6% in 2017-2019 with isolates predominantly from regions with few pig farms. wgMLST reveals the presence of 35 genogroups with distinct resistance, virulence gene profiles and specimen origin. Typing shows prolonged persistent MRSA carriage with a mean carriage period of 407 days. There is a clear spatial and a weak temporal relationship between isolates that clustered in wgMLST, indicative for regional spread of MRSA strains.Conclusions.Using molecular characterization, this exceptionally large study shows genomic changes in the MRSA population at the national level. It reveals waxing and waning of types and genogroups and an increasing proportion of PVL-positive MRSA.A group of bacteria that cause difficult-to-treat infections in humans is methicillin-resistant Staphylococcus aureus (MRSA). The aim of this study was to monitor changes in the spread of MRSA, their disease causing potential and resistance to antibiotics used to treat MRSA infections. MRSA from patients and their contacts in the Netherlands were collected over a period of 12 years and characterized. This revealed new types of MRSA emerged and others disappeared. An increasing number of MRSA produces a protein called PVL toxin, enabling MRSA to cause more severe infections. Also, some people appear to carry MRSA without any disease for more than a year. These findings suggest an increasing disease potential of MRSA and possible unnoticed sources of infection. Consequently, it is important to maintain monitoring of these infections to minimize MRSA spread.Schouls et al. characterize 43,321 methicillin-resistant Staphylococcus aureus (MRSA) isolates obtained between 2008 and 2019 in the Netherlands. Genomic changes occur in the MRSA population, with increases in the proportion of PVL-positive MRSA.Molecular basis of bacterial pathogenesis, virulence factors and antibiotic resistanc

Global data set of long-term summertime vertical temperature profiles in 153 lakes

peer reviewedClimate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change. © 2021, The Author(s)

Avian spatial responses to forest spatial heterogeneity at the landscape level: conceptual and statistical challenges

An explicit consideration of spatial structure in ecological studies plays an increasingly important role in attempts to better understand and manage ecological processes, such as deforestation, forest homogenization, and escalating landscape heterogeneity. The goal of this chapter is to quantify the relationship between forest cover data and ovenbird (Seiurus aurocapilla) abundance¿a ground nesting passerine that breeds in contiguous forests¿in southern Ontario (Canada). To quantify this relationship, we use the Ontario Breeding Bird Atlas 2001¿2005 and compare two spatially explicit modeling methods: geographically weighted regression (GWR) and regression kriging (RK). We show how GWR and RK account for residual spatial autocorrelation in models of forest cover and ovenbird abundance, and we examine the insights they provide. Based on regression kriging, we found that 68 % (adjusted R 2 ) of ovenbird abundance was explained by forest cover, which was an improvement over ordinary least-square regression (adjusted R 2 = 43%), but was not uniformly better than variance explained by GWR in different subregions. These results emphasize the importance of both performing spatial data exploration prior to statistical analyses and accounting for spatial structure during the analysi

A geostatistical approach to data harmonization - Application to radioactivity exposure data

Environmental issues such as air, groundwater pollution and climate change are frequently studied at spatial scales that cross boundaries between political and administrative regions. It is common for different administrations to employ different data collection methods. If these differences are not taken into account in spatial interpolation procedures then biases may appear and cause unrealistic results. The resulting maps may show misleading patterns and lead to wrong interpretations. Also, errors will propagate when these maps are used as input to environmental process models. In this paper we present and apply a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data sources. The associated best linear unbiased estimation and prediction (BLUE and BLUP) equations are presented and it is shown that these lead to harmonized maps from which estimated biases are removed. The methodology is illustrated with an example of country bias removal in a radioactivity exposure assessment for four European countries. The application also addresses multicollinearity problems in data harmonization, which arise when both artificial bias factors and natural drifts are present and cannot easily be distinguished. Solutions for handling multicollinearity are suggested and directions for further investigations proposed