Optimistic Optimization of Gaussian Process Samples

Bayesian optimization is a popular formalism for global optimization, but its computational costs limit it to expensive-to-evaluate functions. A competing, computationally more efficient, global optimization framework is optimistic optimization, which exploits prior knowledge about the geometry of the search space in form of a dissimilarity function. We investigate to which degree the conceptual advantages of Bayesian Optimization can be combined with the computational efficiency of optimistic optimization. By mapping the kernel to a dissimilarity, we obtain an optimistic optimization algorithm for the Bayesian Optimization setting with a run-time of up to $\mathcal{O}(N \log N)$. As a high-level take-away we find that, when using stationary kernels on objectives of relatively low evaluation cost, optimistic optimization can be strongly preferable over Bayesian optimization, while for strongly coupled and parametric models, good implementations of Bayesian optimization can perform much better, even at low evaluation cost. We argue that there is a new research domain between geometric and probabilistic search, i.e. methods that run drastically faster than traditional Bayesian optimization, while retaining some of the crucial functionality of Bayesian optimization.Comment: 10 pages, 6 figure

Multisource Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS)

The Arctic region has undergone warming at a rate more than 3 times higher than the global average. This warming has led to the degradation of near-surface permafrost, resulting in decreased ground stability. This instability not only poses a primary hazard to Arctic infrastructure and human-impacted areas but can also lead to secondary ecological hazards from infrastructure failure associated with hazardous materials. This development underscores the need for a comprehensive inventory of critical infrastructure and human-impacted areas. The inventory should be linked to environmental data to assess their susceptibility to permafrost degradation as well as the ecological consequences that may arise from infrastructure failure. Here, we provide such an inventory for Alaska, a vast state covering approximately 1.7 × 106 km2, with a population of over 733 000 people and a history of industrial development on permafrost. Our Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS) integrates data from (i) the Sentinel-1/2-derived Arctic Coastal Human Impact dataset (SACHI); (ii) OpenStreetMap (OSM); (iii) the pan-Arctic Catchment Database (ARCADE); (iv) a dataset of permafrost extent, probability and mean annual ground temperatures; and (v) the Contaminated Sites Database and reports to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska. The integration process included harmonizing spatial references, extents and geometries across all the datasets as well as incorporating a uniform usage type classification scheme for the infrastructure data. Additionally, we employed text-mining techniques to generate complementary geospatial data from textual reports on contaminated sites, including details on contaminants, cleanup duration and the affected media. The combination of SACHI and OSM enhanced the detail of the usage type classification for infrastructure from 5 to 13 categories, allowing the identification of elements critical to Arctic communities beyond industrial sites. Further, the new inventory integrates the high spatial detail of OSM with the unbiased infrastructure detection capability of SACHI, accurately representing 94 % of the polygonal infrastructure and 78 % of the linear infrastructure, respectively. The SIRIUS dataset is presented as a GeoPackage, enabling spatial analysis and queries of its components, either as a function of or in combination with one another. The dataset is available on Zenodo at https://doi.org/10.5281/zenodo.8311243 (Kaiser et al., 2023).Bundesministerium für Bildung und ForschungHumboldt-Universität zu BerlinHorizon 2020Peer Reviewe

The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure

Ground subsidence and erosion processes caused by permafrost thaw pose a high risk to infrastructure in the Arctic. Climate warming is increasingly accelerating the thawing of permafrost, emphasizing the need for thorough monitoring to detect damages and hazards at an early stage. The use of unoccupied aerial vehicles (UAVs) allows a fast and uncomplicated analysis of sub-meter changes across larger areas compared to manual surveys in the field. In our study, we investigated the potential of photogrammetry products derived from imagery acquired with off-the-shelf UAVs in order to provide a low-cost assessment of the risks of permafrost degradation along critical infrastructure. We tested a minimal drone setup without ground control points to derive high-resolution 3D point clouds via structure from motion (SfM) at a site affected by thermal erosion along the Dalton Highway on the North Slope of Alaska. For the sub-meter change analysis, we used a multiscale point cloud comparison which we improved by applying (i) denoising filters and (ii) alignment procedures to correct for horizontal and vertical offsets. Our results show a successful reduction in outliers and a thorough correction of the horizontal and vertical point cloud offset by a factor of 6 and 10, respectively. In a defined point cloud subset of an erosion feature, we derive a median land surface displacement of (Formula presented.) m from 2018 to 2019. Projecting the development of the erosion feature, we observe an expansion to NNE, following the ice-wedge polygon network. With a land surface displacement of (Formula presented.) m and an alignment root mean square error of (Formula presented.) m, we find our workflow is best suitable for detecting and quantifying rapid land surface changes. For a future improvement of the workflow, we recommend using alternate flight patterns and an enhancement of the point cloud comparison algorithm

Organic Matter in the Surface Microlayer: Insights From a Wind Wave Channel Experiment

The surface microlayer (SML) is the uppermost thin layer of the ocean and influencing interactions between the air and sea, such as gas exchange, atmospheric deposition and aerosol emission. Organic matter (OM) plays a key role in air-sea exchange processes, but studying how the accumulation of organic compounds in the SML relates to biological processes is impeded in the field by a changing physical environment, in particular wind speed and wave breaking. Here, we studied OM dynamics in the SML under controlled physical conditions in a large annular wind wave channel, filled with natural seawater, over a period of 26 days. Biology in both SML and bulk water was dominated by bacterioneuston and -plankton, respectively, while autotrophic biomass in the two compartments was very low. In general, SML thickness was related to the concentration of dissolved organic carbon (DOC) but not to enrichment of DOC or of specific OM components in the SML. Pronounced changes in OM enrichment and molecular composition were observed in the course of the study and correlated significantly to bacterial abundance. Thereby, hydrolysable amino acids, in particular arginine, were more enriched in the SML than combined carbohydrates. Amino acid composition indicated that less degraded OM accumulated preferentially in the SML. A strong correlation was established between the amount of surfactants coverage and γ-aminobutric acid, suggesting that microbial cycling of amino acids can control physiochemical traits of the SML. Our study shows that accumulation and cycling of OM in the SML can occur independently of recent autotrophic production, indicating a widespread biogenic control of process across the air-sea exchange

Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community

A key challenge in ecology is to understand how nutrients and light affect the biodiversity and community structure of phytoplankton and plant communities. According to resource competition models, ratios of limiting nutrients are major determinants of species composition. At high nutrient levels, however, species interactions may shift to competition for light, which might make nutrient ratios less relevant. The "nutrient-load hypothesis" merges these two perspectives, by extending the classic model of competition for two nutrients to include competition for light. Here, we test five key predictions of the nutrient-load hypothesis using multispecies competition experiments. A marine phytoplankton community sampled from the North Sea was inoculated in laboratory chemostats provided with different nitrogen (N) and phosphorus (P) loads to induce either single resource limitation or co-limitation of N, P, and light. Four of the five predictions were validated by the experiments. In particular, different resource limitations favored the dominance of different species. Increasing nutrient loads caused changes in phytoplankton species composition, even if the N:P ratio of the nutrient loads remained constant, by shifting the species interactions from competition for nutrients to competition for light. In all treatments, small species became dominant whereas larger species were competitively excluded, supporting the common view that small cell size provides a competitive advantage under resource-limited conditions. Contrary to expectation, all treatments led to coexistence of diatoms, cyanobacteria and green algae, resulting in a higher diversity of species than predicted by theory. Because the coexisting species comprised three phyla with different photosynthetic pigments, we speculate that niche differentiation in the light spectrum might play a role. Our results show that mechanistic resource competition models that integrate nutrient-based and light-based approaches provide an important step forward to understand and predict how changing nutrient loads affect community composition

Dynamics of organic matter and bacterial activity in the Fram Strait during summer and autumn

The Arctic Ocean is considerably affected by the consequences of global warming, including more extreme seasonal fluctuations in the physical environment. So far, little is known about seasonality in Arctic marine ecosystems in particular microbial dynamics and cycling of organic matter. The limited characterization can be partially attributed to logistic difficulties of sampling in the Arctic Ocean beyond the summer season. Here, we investigated the distribution and composition of dissolved organic matter (DOM), gel particles and heterotrophic bacterial activity in the Fram Strait during summer and autumn. Our results revealed that phytoplankton biomass influenced the concentration and composition of semi-labile dissolved organic carbon (DOC), which strongly decreased from summer to autumn. The seasonal decrease in bioavailability of DOM appeared to be the dominant control on bacterial abundance and activity, while no temperature effect was determined. Additionally, there were clear differences in transparent exopolymer particles (TEP) and Coomassie Blue stainable particles (CSP) dynamics. The amount of TEP and CSP decreased from summer to autumn, but CSP was relatively enriched in both seasons. Our study therewith indicates clear seasonal differences in the microbial cycling of organic matter in the Fram Strait. Our data may help to establish baseline knowledge about seasonal changes in microbial ecosystem dynamics to better assess the impact of environmental change in the warming Arctic Ocean

Towards representing thermokarst processes in land surface models

Large-scale Earth system and land surface models often lack an adequate representation of subgrid-scale processes in permafrost landscapes. Small-scale processes such as thermokarst formation might, however, considerably impact the energy and carbon budgets in way which is not resolved within large-scale models. Since a spatially high-resolved simulation of such processes is not feasible, novel techniques for up-scaling subgrid processes are demanded. Within this work a one-dimensional model of the ground thermal regime of land surfaces, CryoGrid 3, is employed to conceptually represent small-scale features of permafrost landscapes, particularly those related to thermokarst. For example, the model has been shown to adequately describe the degradation of permafrost underneath waterbodies in a warming climate. Using tiling approaches such point-wise realizations can be up-scaled in a statistical way in order to represent larger land surface units. The model development is closely linked to field campaigns to the Lena River Delta in Siberia which offers very diverse land surface features such as polygonal tundra and thermos-erosional valleys. These features are related to the region’s diverse soil stratigraphies, in particular the occurrence of ice-rich ground. Combining field measurements with modelling ultimately allows an improvement in the qualitative and quantitative understanding of the typical geomorphological processes in permafrost landscapes and their representation in large-scale models

Agar Contact Method as a Valuable Tool to Identify Slaughter Hygiene Deficiencies along the Slaughter Process by Longitudinally Sampling Pig Skin Surfaces

Examinations of total viable counts (TVCs) and Salmonella spp. on the skin of individual pigs during the slaughter process are useful to identify abattoir-specific risk factors for (cross-)contamination. At seven process stages (lairage to before chilling), pigs were bacteriologically investigated by repeatedly sampling the same animals using the agar contact method. The mean TVC of all pigs increased significantly at the first three tested process stages (mean count, after delivery: 5.70 log cfu/cm2, after showering: 6.27 log cfu/cm2, after stunning: 6.48 log cfu/cm2). Significant mean TVC reductions occurred after scalding/dehairing (mean count: 3.71 log cfu/cm2), after singeing/flaming (2.70 log cfu/cm2), and after evisceration (2.44 log cfu/cm2) compared with the respective preceding process stages. At the end of the slaughter line and before chilling, the mean TVC was 2.33 log cfu/cm2, showing that the slaughter process reduced contamination significantly. The slaughter process effectively reduced even very high levels of incoming TVCs, since at the individual animal level, at the end of the slaughter process, there was no difference in the TVCs of animals with initially high and initially low TVCs. Additionally, 12 Salmonella spp. isolates were recovered from 12 different pigs, but only until the stage after scalding/dehairing. Overall, the agar contact method used is valuable for detecting hygiene deficiencies at slaughter, and is animal-equitable, practical, and suitable for use on live animals

From Ecological Stoichiometry to Biochemical Composition: Variation in N and P Supply Alters Key Biosynthetic Rates in Marine Phytoplankton

One of the major challenges in ecological stoichiometry is to establish how environmental changes in resource availability may affect both the biochemical composition of organisms and the species composition of communities. This is a pressing issue in many coastal waters, where anthropogenic activities have caused large changes in riverine nutrient inputs. Here we investigate variation in the biochemical composition and synthesis of amino acids, fatty acids (FA), and carbohydrates in mixed phytoplankton communities sampled from the North Sea. The communities were cultured in chemostats supplied with different concentrations of dissolved inorganic nitrogen (DIN) and phosphorus (DIP) to establish four different types of resource limitations. Diatoms dominated under N-limited, N+P limited and P-limited conditions. Cyanobacteria became dominant in one of the N-limited chemostats and green algae dominated in the one P-limited chemostat and under light-limited conditions. Changes in nutrient availability directly affected amino acid content, which was lowest under N and N+P limitation, higher under P-limitation and highest when light was the limiting factor. Storage carbohydrate content showed the opposite trend and storage FA content seemed to be co-dependent on community composition. The synthesis of essential amino acids was affected under N and N+P limitation, as the transformation from non-essential to essential amino acids decreased at DIN:DIP ≤ 6. The simple community structure and clearly identifiable nutrient limitations confirm and clarify previous field findings in the North Sea. Our results show that different phytoplankton groups are capable of adapting their key biosynthetic rates and hence their biochemical composition to different degrees when experiencing shifts in nutrient availability. This will have implications for phytoplankton growth, community structure, and the nutritional quality of phytoplankton as food for higher trophic levels