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This paper was initiated by a multidisciplinary Topic Workshop in the frame of the Deutsche Forschungsgemeinschaft Priority Program 1158 “Antarctic Research with Comparative Investigations in Arctic Ice Areas”, and hence it represents only the national view without claiming to be complete but is intended to provide awareness and suggestions for the current discussion on so-called big data in many scientific fields. The importance of the polar regions and their essential role for the Earth system are both undoubtedly recognized. However, dramatic changes in the climate and environment have been observed first in the Arctic and later in Antarctica over the past few decades. While important data have been collected and observation networks have been built in Antarctica and the Southern Ocean, this is a relatively data-scarce region due to the challenges of remote data acquisition, expensive labor, and harsh environmental conditions. There are many approaches crossing multiple scientific disciplines to better understand Antarctic processes; to evaluate ongoing climatic and environmental changes and their manifold ecological, physical, chemical, and geological consequences; and to make (improved) predictions. Together, these approaches generate very large, multivariate data sets, which can be broadly classified as “Antarctic big data”. For these large data sets, there is a pressing need for improved data acquisition, curation, integration, service, and application to support fundamental scientific research. Based on deficiencies in crossing disciplines and to attract further interest in big data in Antarctic sciences, this article will (i) describe and evaluate the current status of big data in various Antarctic-related scientific disciplines, (ii) identify current gaps, (iii) and provide solutions to fill these gaps.

The ExoClock project has been created to increase the efficiency of the Ariel mission. It will achieve this by continuously monitoring and updating the ephemerides of Ariel candidates, in order to produce a consistent catalog of reliable and precise ephemerides. This work presents a homogenous catalog of updated ephemerides for 450 planets, generated by the integration of ∼18,000 data points from multiple sources. These sources include observations from ground-based telescopes (the ExoClock network and the Exoplanet Transit Database), midtime values from the literature, and light curves from space telescopes (Kepler, K2, and TESS). With all the above, we manage to collect observations for half of the postdiscovery years (median), with data that have a median uncertainty less than 1 minute. In comparison with the literature, the ephemerides generated by the project are more precise and less biased. More than 40% of the initial literature ephemerides had to be updated to reach the goals of the project, as they were either of low precision or drifting. Moreover, the integrated approach of the project enables both the monitoring of the majority of the Ariel candidates (95%), and also the identification of missing data. These results highlight the need for continuous monitoring to increase the observing coverage of the candidate planets. Finally, the extended observing coverage of planets allows us to detect trends (transit-timing variations) for a sample of 19 planets. All the products, data, and codes used in this work are open and accessible to the wider scientific community.

Abstract Background Improved, multimodal treatment strategies have been shown to increase cure rates in cancer patients. Those who survive cancer as a child, adolescent or young adult (CAYA), are at a higher risk for therapy-, or disease-related, late or long-term effects. The CARE for CAYA-Program has been developed to comprehensively assess any potential future problems, to offer need-based preventative interventions and thus to improve long-term outcomes in this particularly vulnerable population. Methods The trial is designed as an adaptive trial with an annual comprehensive assessment followed by needs stratified, modular interventions, currently including physical activity, nutrition and psycho-oncology, all aimed at improving the lifestyle and/or the psychosocial situation of the patients. Patients, aged 15–39 years old, with a prior cancer diagnosis, who have completed tumour therapy and are in follow-up care, and who are tumour free, will be included. At baseline (and subsequently on an annual basis) the current medical and psychosocial situation and lifestyle of the participants will be assessed using a survey compiled of various validated questionnaires (e.g. EORTC QLQ C30, NCCN distress thermometer, PHQ-4, BSA, nutrition protocol) and objective parameters (e.g. BMI, WHR, co-morbidities like hyperlipidaemia, hypertension, diabetes), followed by basic care (psychological and lifestyle consultation). Depending on their needs, CAYAs will be allocated to preventative interventions in the above-mentioned modules over a 12-month period. After 1 year, the assessment will be repeated, and further interventions may be applied as needed. During the initial trial phase, the efficacy of this approach will be compared to standard care (waiting list with intervention in the following year) in a randomized study. During this phase, 530 CAYAs will be included and 320 eligible CAYAs who are willing to participate in the interventions will be randomly allocated to an intervention. Overall, 1500 CAYAs will be included and assessed. The programme is financed by the innovation fund of the German Federal Joint Committee and will be conducted at 14 German sites. Recruitment began in January 2018. Discussion CAYAs are at high risk for long-term sequelae. Providing structured interventions to improve lifestyle and psychological situation may counteract against these risk factors. The programme serves to establish uniform regular comprehensive assessments and need-based interventions to improve long-term outcome in CAYA survivors. Trial registration Registered at the German Clinical Trial Register (ID: DRKS00012504, registration date: 19th January 2018).

Mass gatherings create settings conducive to infectious disease transmission. Empirical data to model infectious disease transmission at mass gatherings are limited. Video-analysis technology could be used to generate data on social mixing patterns needed for simulating influenza transmission at mass gatherings. We analyzed short video recordings of persons attending the GameFest event at a university in Troy, New York, in April 2013 to demonstrate the feasibility of this approach. Attendees were identified and tracked during three randomly selected time periods using an object-tracking algorithm. Tracks were analyzed to calculate the number and duration of unique pairwise contacts. A contact occurred each time two attendees were within 2 m of each other. We built and tested an agent-based stochastic influenza simulation model assuming two scenarios of mixing patterns in a geospatially accurate representation of the event venue —one calibrated to the mean cumulative contact duration estimated from GameFest video recordings and the other using a uniform mixing pattern. We compared one-hour attack rates (i.e., becoming infected) generated from these two scenarios following the introduction of a single infectious seed. Across the video recordings, 278 attendees were identified and tracked, resulting in 1,247 unique pairwise contacts with a cumulative mean contact duration of 74.76 s (SD: 80.71). The one-hour simulated mean attack rates were 2.17 % (95 % CI:1.45 – 2.82) and 0.21 % (95 % CI: 0.14 – 0.28) in the calibrated and uniform mixing model scenarios, respectively. We simulated influenza transmission at the GameFest event using social mixing data objectively captured through video-analysis technology. Microlevel geospatially accurate simulations can be used to assess the layout of event venues on social mixing and disease transmission. Future work can expand on this demonstration project to larger spatial and temporal scenes in more diverse settings.

Abstract In recent decades, many bumble bee species have declined due to changes in habitat, climate, and pressures from pathogens, pesticides, and introduced species. The western bumble bee (Bombus occidentalis), once common throughout western North America, is a species of concern and will be considered for listing by the U.S. Fish and Wildlife Service (USFWS) under the Endangered Species Act (ESA). We attempt to improve alignment of data collection and research with USFWS needs to consider redundancy, resiliency, and representation in the upcoming species status assessment. We reviewed existing data and literature on B. occidentalis, highlighting information gaps and priority topics for research. Priorities include increased knowledge of trends, basic information on several life‐history stages, and improved understanding of the relative and interacting effects of stressors on population trends, especially the effects of pathogens, pesticides, climate change, and habitat loss. An understanding of how and where geographic range extent has changed for the two subspecies of B. occidentalis is also needed. We outline data that could be easily collected in other research projects that would increase their utility for understanding range‐wide trends of bumble bees. We modeled the overall trend in occupancy from 1998 to 2018 of Bombus occidentalis within the continental United States using existing data. The probability of local occupancy declined by 93% over 21 yr from 0.81 (95% CRI = 0.43, 0.98) in 1998 to 0.06 (95% CRI = 0.02, 0.16) in 2018. The decline in occupancy varied spatially by landcover and other environmental factors. Detection rates vary in both space and time, but peak detection across the continental United States occurs in mid‐July. We found considerable spatial gaps in recent sampling, with limited sampling in many regions, including most of Alaska, northwestern Canada, and the southwestern United States. We therefore propose a sampling design to address these gaps to best inform the ESA species status assessment through improved assessment of how the spatial distribution of stressors influences occupancy changes. Finally, we request involvement via data sharing, participation in occupancy sampling with repeated visits to distributed survey sites, and complementary research to address priorities outlined in this paper.

Abstract Climate oscillations have left a significant impact on the patterns of genetic diversity observed in numerous taxa. In this study, we examine the effect of Quaternary climate instability on population genetic variability of a bumble bee pollinator species, Bombus huntii in western North America. Pleistocene and contemporary B. huntii habitat suitability (HS) was estimated with an environmental niche model (ENM) by associating 1,035 locality records with 10 bioclimatic variables. To estimate genetic variability, we genotyped 380 individuals from 33 localities at 13 microsatellite loci. Bayesian inference was used to examine population structure with and without a priori specification of geographic locality. We compared isolation by distance (IBD) and isolation by resistance (IBR) models to examine population differentiation within and among the Bayesian inferred genetic clusters. Furthermore, we tested for the effect of environmental niche stability (ENS) on population genetic diversity with linear regression. As predicted, high‐latitude B. huntii habitats exhibit low ENS when compared to low‐latitude habitats. Two major genetic clusters of B. huntii inhabit western North America: (a) a north genetic cluster predominantly distributed north of 28°N and (b) a south genetic cluster distributed south of 28°N. In the south genetic cluser, both IBD and IBR models are significant. However, in the north genetic cluster, IBD is significant but not IBR. Furthermore, the IBR models suggest that low‐latitude montane populations are surrounded by habitat with low HS, possibly limiting dispersal, and ultimately gene flow between populations. Finally, we detected high genetic diversity across populations in regions that have been climatically unstable since the last glacial maximum (LGM), and low genetic diversity across populations in regions that have been climatically stable since the LGM. Understanding how species have responded to climate change has the potential to inform management and conservation decisions of both ecological and economic concerns.

Climate change is predicted to increase the risk of biological invasions by increasing the availability of climatically suitable regions for invasive species. Endemic species on oceanic islands are particularly sensitive to the impact of invasive species due to increased competition for shared resources and disease spread. In our study, we used an ensemble of species distribution models (SDM) to predict habitat suitability for invasive bees under current and future climate scenarios in Hawai’i. SDMs projected on the invasive range were better predicted by georeferenced records from the invasive range in comparison to invasive SDMs predicted by records from the native range. SDMs estimated that climatically suitable regions for the eight invasive bees explored in this study will expand by ~934.8% (±3.4% SE). Hotspots for the invasive bees are predicted to expand toward higher elevation regions, although suitable habitat is expected to only progress up to 500 m in elevation in 2070. Given our results, it is unlikely that invasive bees will interact directly with endemic bees found at >500 m in elevation in the future. Management and conservation plans for endemic bees may be improved by understanding how climate change may exacerbate negative interactions between invasive and endemic bee species.

Recently published approaches to SLAM algorithms process laser sensor measurements and output a map as a point cloud of the environment. Often the actual precision of the map remains unclear, since SLAMalgorithms apply local improvements to the resulting map. Unfortunately, it is not trivial to compare the performance of SLAMalgorithms objectively, especially without an accurate ground truth. This paper presents a novel benchmarking technique that allows to compare a precise map generated with an accurate ground truth trajectory to a map with a manipulated trajectory which was distorted by different forms of noise. The accurate ground truth is acquired by mounting a laser scanner on an industrial robotic arm. The robotic arm is moved on a predefined path while the position and orientation of the end-effector tool are monitored. During this process the 2D profile measurements of the laser scanner are recorded in six degrees of freedom and afterwards used to generate a precise point cloud of the test environment. For benchmarking, an offline continuous-time SLAM algorithm is subsequently applied to remove the inserted distortions. Finally, it is shown that the manipulated point cloud is reversible to its previous state and is slightly improved compared to the original version, since small errors that came into account by imprecise assumptions, sensor noise and calibration errors are removed as well.

Bumble bees (Bombus Latrielle) are significant pollinators of flowering plants due to their large body size, abundant setae, and generalist foraging strategies. However, shared setal coloration patterns among closely and distantly related bumble bee species makes identification notoriously difficult. The advent of molecular genetic techniques has increased our understanding of bumble bee evolution and taxonomy, and enables effective conservation policy and management. Individuals belonging to the North American Bombus fervidus species-complex (SC) are homogenous in body structure but exhibit significant body color phenotype variation across their geographic distribution. Given the uncertainty of the genealogical boundaries within the SC, some authors have synonymized all members of the B. fervidus SC within a single taxon, while others propose an alternative two taxa hypothesis. Operating under the phylogenetic species concept, our analysis supports the hypothesis that there are two independent lineages of bumble bees within the B. fervidus SC. With the current evidence, however, it is not possible to assign valid names to either of them, because both lineages include the color phenotypes found in the original species descriptions of B. fervidus and B. californicus. Cryptic speciation does not seem to be the product of Müllerian mimicry between the clades, because diverging coloration patterns are observed when the distribution of the clades overlaps. Furthermore, within each lineage there is evidence for strong population differentiation that is correlated with geographic distribution rather than color phenotype. In our study, we demonstrate the importance of obtaining a broad sample of multiple populations when conducting lower-level phylogenetic analyses. In addition to improving our knowledge of bumble bee diversification patterns, characterizing the evolutionary history of these pollinators provides the foundation needed to guide contemporary conservation assessments and management strategies.

Digital elevation models (DEMs) and height measurements are broadly used in environmental studies. Two common elevation sources are the Ice Cloud and land elevation Satellite (ICESat), which acquired laser range measurements with the Geoscience Laser Altimeter System (GLAS) across the globe and elevation data from the Shuttle Radar Topography Mission (SRTM). Current developments of small unmanned aerial vehicles (UAV) provide the opportunity to collect aerial images of remote areas at a high spatial resolution. These can be further processed to digital surface models by stereophotogrammetry and provide a reliable data source to evaluate coarse scale Digital Elevation Models (DEMs). This study compares ICESat/GLAS and SRTM90 elevation data against photogrammetric terrain heights within GLAS footprints on high altitudes on the East Tibetan Plateau. Without vegetation-bias, we were able to examine height differences under different topographic conditions and of different acquisition dates. Several resampling techniques were applied to SRTM90 data and averaged height within each footprint was calculated. ICESat/GLAS heights (n = 148) are most similar to UAV data based elevations with an averaged difference of −0.8m ±3.1m. Results furthermore indicate the validity of ICESat/GLAS heights, which are usually removed from analyses by applying different quality flags. Smallest difference of SRTM90 to UAV based heights could be observed by a natural neighbour resampling technique (averaged 3.6m ±14m), whereat other techniques achieved quite similar results. It can be confirmed that within a range of 3,800–4,200m above mean sea level the ICESat/GLAS heights are a precise source to determine elevation at footprint geolocation.

For reasons of documentation, management and certification there is a high interest in efficient inventories of palm plantations on the single plant level. Recent developments in unmanned aerial vehicle (UAV) technology facilitate spatial and temporal flexible acquisition of high resolution 3D data. Common single tree detection approaches are based on Very High Resolution (VHR) satellite or Airborne Laser Scanning (ALS) data. However, VHR data is often limited to clouds and does commonly not allow for height measurements. VHR and in particualar ALS data are characterized by high relatively high acquisition costs. Sperlich et al. (2013) already demonstrated the high potential of UAV-based photogrammetric point clouds for single tree detection using pouring algorithms. This approach was adjusted and improved for an application on palm plantation. The 9.4ha test site on Tarawa, Kiribati, comprised densely scattered growing palms, as well as abundant undergrowth and trees. Using a standard consumer grade camera mounted on an octocopter two flight campaigns at 70m and 100m altitude were performed to evaluate the effect Ground Sampling Distance (GSD) and image overlap. To avoid comission errors and improve the terrain interpolation the point clouds were classified based on the geometric characteristics of the classes, i.e. (1) palm, (2) other vegetation (3) and ground. The mapping accuracy amounts for 86.1 % for the entire study area and 98.2 % for dense growing palm stands. We conclude that this flexible and automatic approach has high capabilities for operational use.

Das Reallabor „Wissensdialog Nordschwarzwald“ ist eng an den Nationalpark Schwarzwald gekoppelt: Wie lassen sich Chancen nutzen, die der Nationalpark für die nachhaltige Entwicklung der Region bietet? Was lässt sich aus den im Nationalparkgebiet stattfindenden ökologischen Prozessen lernen? Dies sind die zentralen Forschungsfragen des Projekts. Der vorliegende Beitrag stellt die Herausforderungen des spezifischen transdisziplinären Designs des Reallabors in den Vordergrund. Basierend auf Interviews mit Projektbeteiligten, dem Austausch in Projektgremien und eigenen Beobachtungen reflektieren die AutorInnen zentrale Designelemente sowie Kontextbedingungen des Projektes. Angesichts der besonderen Gegebenheiten des ländlichen Raumes postulieren sie den Bedarf, eine stärker an diesen angepasste Reallaborforschung zu entwickeln.

In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground- and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75 [m2] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45° were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55 [m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14–42 [mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9 [ha] test area, 102 individual trees above 7 [cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under- or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as in the TLS-point cloud. Only few stems were considered to be fully reconstructed. From the comparison of reconstruction achievement with respect to height above ground, we can state that reconstruction accuracy decreased in the crown layer of the stand. In addition we were cutting 50 [cm] slices in z-direction and applied a robust cylinder fit to the stem slices. Radii of the TLS-cloud and the SFM-cloud surprisingly correlated well with a Pearson's correlation coefficient of r = 0.696. This first study showed promising results for UAV-based forest structure modelling. Yet, there is a demand for additional research with regard to vegetation stages, flight pattern, processing setup and the utilisation of spectral information.

Forest structure was modelled to explore the nonparametric approaches for inventory of small forests. Models were built using information from airborne optical and LiDAR data across two adjacent forest sites using the remote sensing data collected by a similar instrument. Off-site samples from the inventoried reference stands were combined with drawn samples from the target area for modelling responses. The applied combinations included two imputation methods, three sampling designs, and two predictor subset sizes, where a Genetic Algorithm (GA) was used to prune the predictors. Diagnostic tools including Root Mean Square Error, bias and Standard Error of Imputation were employed to evaluate the results. Results showed that Random Forests produced more accurate results than Most Similar Neighbour inference, yet was slightly more biased than MSN. Appending systematically stratified samples from the target dataset yielded more accurate results, yet it was most influential up to a low-to-medium intensity. The use of pruned predictors resulted in reduced bias. By bootstrapping the RMSE, the majority of the simulations lied within the confidence intervals. The applied methods show positive potential towards producing spatial models of forest structure.

The movements of the sand dunes are considered as a threat for roads, irrigation networks, water resources, urban areas, agriculture and infrastructures. The main objectives of this study are to develop a new GIS-based model for automated extraction of sand dune encroachment using remote sensing data and to assess the rate of sand dune movement. To monitor and assess the movements of sand dunes in Dakhla oases area, multi-temporal satellite images and a GIS-developed model, using Python script in Arc GIS, were used. The satellite images (SPOT images, 1995 and 2007) were geo-rectified using Erdas Imagine. Image subtraction was performed using spatial analyst in Arc GIS, the result of image subtraction obtains the sand dune movement between the two dates. The raster and vector shape of sand dune migration was automatically extracted using spatial analyst tools. The frontiers of individual dunes were measured at different dates and movement rates were analyzed in GIS. The ModelBuilder in Arc GIS was used in order to create a user friendly tool. The custom built model window is easy to handle by any user who wishes to adapt the model in his work. It was found that the rate of sand dune movement ranged between 3 and 9 m per year. The majority of sand dunes have a rate movement between 0 and 6 m and very few dunes had a movement rate between 6 and 9 m. Integrating remote sensing and GIS provided the necessary information for determining the minimum, maximum, mean, rate and area of sand dune migration.

A través de este trabajo se efectúa una revisión bibliográfica acerca de esta tradicional genodermatosis, que comprende un recorrido a través de la historia, epidemiología y fisiopatología desde la intimidad de la genética, pasando por los procesos bioquímicos actuales. Se repasa la clínica en forma exhaustiva, donde se aplica mi experiencia personal en aspectos evolutivos y terapéuticos.Through this work the authors reviewed the literature about traditional genodermatosis, which includes a journey through history, epidemiology and pathophysiology from the privacy of genetic, biochemical processes through today. The clinic is reviewed exhaustively and is applied my personal experience, in developmental and therapeutic aspects.

The Rancho La Brea Tar Pits is the world's richest and most important Late Pleistocene fossil locality and best renowned for numerous fossil mammals and birds excavated over the past century. Less researched are insects, even though these specimens frequently serve as the most valuable paleoenvironemental indicators due to their narrow climate restrictions and life cycles. Our goal was to examine fossil material that included insect-plant associations, and thus an even higher potential for significant paleoenviromental data. Micro-CT scans of two exceptionally preserved leafcutter bee nest cells from the Rancho La Brea Tar Pits in Los Angeles, California reveal intact pupae dated between ∼23,000-40,000 radiocarbon years BP. Here identified as best matched to Megachile (Litomegachile) gentilis Cresson (Hymenoptera: Megachilidae) based on environmental niche models as well as morphometrics, the nest cells (LACMRLP 388E) document rare preservation and life-stage. The result of complex plant-insect interactions, they offer new insights into the environment of the Late Pleistocene in southern California. The remarkable preservation of the nest cells suggests they were assembled and nested in the ground where they were excavated. The four different types of dicotyledonous leaves used to construct the cells were likely collected in close proximity to the nest and infer a wooded or riparian habitat with sufficient pollen sources for larval provisions. LACMRLP 388E is the first record of fossil Megachile Latreille cells with pupae. Consequently, it provides a pre-modern age location for a Nearctic group, whose phylogenetic relationships and biogeographic history remain poorly understood. Megachile gentilis appears to respond to climate change as it has expanded its distribution across elevation gradients over time as estimated by habitat suitability comparisons between low and high elevations; it currently inhabits mesic habitats which occurred at a lower elevation during the Last Glacial Maximum ∼21,000 years ago. Nevertheless, the broad ecological niche of M. gentilis appears to have remained stable.