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A data set was produced under this award and submitted for publication at the Arctic Data Center (2026): Title: Interviews on Impacts of the Arctic Cruise Tourism Industry on Nature, Commerce, and Culture in Northern Communities (2021–2025). doi: pending.temporary identifier: urn:uuid:a65f9b84-6113-4799-b12b-4ec975c0b763. Data were collected as part of the NSF research project "NNA Track 1: Collaborative Research: Navigating Impacts of the Arctic Tourism Industry on Nature, Commerce, and Culture in Northern Communities". This project addressed the challenges resulting from rapidly growing cruise-ship tourism encompassing natural, social and built systems of coastal communities in Alaska (Juneau and Nome), Iceland (Akureyri), Norway (Bergen) and Sweden (Visby). The overarching goals of the project were to: 1) systematically document, compare and interpret the ways in which cruise ships in Arctic and adjacent waters are impacting coastal communities; and 2) work together with local knowledge holders and decision makers to develop a set of data driven community-defined indicators to determine policies to enhance local adaptive capacities for identifying and responding to effects from cruise ship tourism. Data collected in this research project include transcripts of interviews, focus groups and listening sessions. These data were collected between 2021 and 2025 using a combination of online platforms and in-person sessions. In-person sessions were held in Juneau, Nome, Akureyri and Bergen during trips by members of the project team. A total of 123 persons were involved, of which 48 were in Juneau, 33 in Nome, 16 on Akureyri, 16 in Bergen and outlying communities, and 10 in Visby. Several commonalities and some differences amongst the responses were found between communities, mainly emphasizing the importance of finding a balance between social, economic and environmental impacts of cruise tourism in the communities, as well as the importance of technology (infrastructure) and public policy (governance) for sustainable cruise tourism at the community level. 

Noncoding structural variations (SVs) exert deleterious phenotypic effects by remodeling chromatin architecture. The nonlinear nature of the remodeling process makes case-by-case prediction of SVs’ effects arduous. This study employs MiChroM, a maximum-entropy polymer model, to systematize the prediction of the architectural effects of SVs. MiChroM trains a pairwise potential matrix on a Hi-C contact map to build an in-silico model of a genomic locus. By applying SVs to our simulated polymer, we investigate the architectural mechanisms involved in SV-associated phenotypic alterations. As our procedure predicts the effects of SVs by training on a wild-type contact map, it does not require Hi-C data from SV-bearing tissue. We benchmark our model on six limb-development-associated structural variations at the EPHA4 locus. Our model correctly predicts changes in key ectopic, disease-associated enhancer-promoter interactions. Analysis of structural ensembles reveals architectural reorganization consistent with prior hypotheses for this set of mutations. To enhance predictive capacity, we develop matrix adjustment techniques grounded in polymer physics and chromatin folding theory. We also employ a simultaneous kinetic/thermodynamic model of chromatin folding by simulating chromatin organizing motors on the polymer. Comparisons of the adjusted-thermodynamic and kinetic/thermodynamic approaches highlight fundamental principles of genome architecture organization. With the EPHA4 mutation as a successful benchmark, we turn our model’s predictive capacities to previously unsimulated structural variations. We predict the effects of architectural SVs to the SOX9 locus and of complex contact domain shuffling SVs. Our predicted SV contact maps are of higher resolution than their experimental counterparts while reproducing the expected ectopic interactions. This procedure enables predictions of the architectural effects of SV at high resolution based solely on already-published WT contact map data. It thus opens up new avenues for laboratory and clinical investigation of genetic disease. 

Abstract An architecture and workflow are proposed and described for extracting geomorphic features from digital terrain model (DTM)‐derived land surface parameters (LSPs) using deep learning (DL)‐based semantic segmentation and integration of LSP calculations into the model architecture to allow for tensor‐ and graphics processing unit (GPU)‐based computation within the DL framework. To characterize terrain patterns at multiple spatial scales, the input DTM can be generalized using Gaussian pyramids (GPs). The LSPs are then provided to the trainable component of the model. The workflow is explored using two examples: valley fill faces resulting from mountaintop removal surface coal mine reclamation, an example of anthropogenic geomorphic features, and sinkholes within a karst landscape, an example of natural geomorphic features. We compare three different, ‘U’‐shaped architectures as the trainable component of the architecture: a traditional, convolutional neural network (CNN)‐based UNet, UNet with a ConvNeXt‐based encoder and attention gates along the skip connections and UNet with a Mamba‐based encoder and a CNN‐based decoder. As a baseline, DL models are compared with a pixel‐based random forest (RF) model using the same LSP feature space. We document improved performance in comparison with the pixel‐based approach and minimal differences among the three DL architectures based on F1 scores. The inclusion of GPs did not have a large impact on predictive performance for either the RF‐ or DL‐based models. Calculating LSPs as part of the model architecture did not greatly increase the computational complexity of the model. Depending on the model configuration, incorporating the LSP calculations into the model architecture increased the number of floating‐point operations (FLOPs) and multiply–accumulate operations (MACs) by 2.8% to 17.6% and increased the saved model size by 1.2 MB. In practice, only DTM data, mapping extent(s) and example features need to be provided during the training process. Disk space usage is decreased by a factor of 6 when not using GPs and 31 when incorporating these multiscale representations. The workflow offers an efficient means to extract geomorphic features from DTMs and can support downstream modelling and research tasks. It has been made available in the R language via the geodl package and Python via the terrainseg package. 

Abstract Predicting physical and chemical erosion rate responses to climate change are an ongoing challenge in geomorphology. A promising approach for investigating this is by measuring transient variations in physical and chemical erosion rates during climatically variable time periods, which can be accomplished by measuring cosmogenic nuclide concentrations and chemical depletion in sedimentary deposits. Interpreting such measurements warrants applying landscape evolution models that track variations in topography, cosmogenic nuclide concentrations and chemical depletion in soils. We applied a recently developed model that tracks these quantities at Little Lake, Oregon. Previous studies documented variations in cosmogenic nuclide concentrations and chemical depletion in paleo‐lake sediments from 50 ka BP to the present, a time interval that includes cooling before the Last Glacial Maximum and warming after it. We extended the model by adding climate‐sensitive parameterizations for mineral dissolution, soil transport by frost heave and soil production by frost cracking. We conducted simulations driven by a paleo‐temperature time series applicable to Little Lake. Simulations showed that a shift to frost heave, frost cracking and temperature‐controlled mineral weathering and alteration elevated¹⁰Be‐inferred denudation rates and lowered chemical depletion fraction (CDF) values comparable to those observed in cores from paleo‐Little Lake. In contrast, introducing a lake with no changes to process operation led to a decline in denudation rates. No single climate‐sensitive process could reproduce both high inferred denudation rates and low CDF, indicating that all of the climate‐sensitive processes modelled in our simulations are needed to explain observed values. Modelled denudation rates increased when the connection between frost cracking intensity and maximum soil production rate was strengthened. The integration of climate‐sensitive processes showed that a handoff from biotically driven processes to frost‐driven ones could induce large, detectable changes in both inferred denudation rate from¹⁰Be and CDF, signalling the potential for globally heterogeneous climate‐denudation rate linkages. 

Our study is a novel implementation of xurography for multi-layer microfluidic device fabrication. We demonstrate the versatility of this approach by presenting several modular 3D vessel-matrix arrangements. 

Abstract Terrestrial cosmogenic nuclides (TCN) are widely employed to infer denudation rates in mountainous landscapes. The calculation of an inferred denudation rate (D_inf) from TCN concentrations is typically performed under the assumptions that denudation rates were steady during TCN accumulation and that soil chemical weathering negligibly impacted soil mineral abundances. In many landscapes, however, denudation rates were not steady and soil composition was significantly impacted by chemical weathering, which complicates interpretation of TCN concentrations. We present a landscape evolution model that computes transient changes in topography, soil thickness, soil mineralogy, and soil TCN concentrations. We used this model to investigate TCN responses in transient landscapes by imposing idealized perturbations in tectonically (rock uplift rate) and climatically sensitive parameters (soil production efficiency, hillslope transport efficiency, and mineral dissolution rate) on initially steady‐state landscapes. These experiments revealed key insights about TCN responses in transient landscapes. (a) Accounting for soil chemical erosion is necessary to accurately calculateD_inf. (b) Responses ofD_infto tectonic perturbations differ from those to climatic perturbations, suggesting that spatial and temporal patterns inD_infare signatures of perturbation type and magnitude. (c) If soil chemical erosion is accounted for, basin‐averagedD_infinferred from TCN in stream sediment closely tracks actual basin‐averaged denudation rate, showing thatD_infis a reasonable proxy for actual denudation rate, even in many transient landscapes. (d) Response times ofD_infto perturbations increase with hillslope length, implying that response times should be sensitive to the climatic, biological, and lithologic processes that control hillslope length. 

Abstract Environmental monitoring and long-term research produce detailed understanding, but its collective effort does not add up to ‘the environment’ and therefore may be difficult to relate to. Local knowledge, by contrast, is multifaceted and relational and therefore can help ground and complement scientific knowledge to reach a more complete and holistic understanding of the environment and changes therein. Today’s societies, however, are increasingly fleeting, with mobility potentially undermining the opportunity to generate rich community knowledge. Here we perform a case study of High Arctic Svalbard, a climate change and environmental science hotspot, using a range of community science methods, including a Maptionnaire survey, focus groups, interviews and cognitive mapping. We show that rich local knowledge on Svalbard could indeed be gathered through community science methods, despite a high level of transience of the local population. These insights complement environmental monitoring and enhance its local relevance. Complex understanding of Svalbard’s ecosystems by the transient local community arose because of strong place attachment, enabling environmental knowledge generation during work and play. We conclude that transience does not necessarily prevent the generation of valuable local knowledge that can enrich and provide connection to scientific understanding of the environment. 

We present the results of a theoretical investigation of the stability and collective vibrations of a two-dimensional hydrodynamic lattice comprised of millimetric droplets bouncing on the surface of a vibrating liquid bath. We derive the linearized equations of motion describing the dynamics of a generic Bravais lattice, as encompasses all possible tilings of parallelograms in an infinite plane-filling array. Focusing on square and triangular lattice geometries, we demonstrate that for relatively low driving accelerations of the bath, only a subset of inter-drop spacings exist for which stable lattices may be achieved. The range of stable spacings is prescribed by the structure of the underlying wavefield. As the driving acceleration is increased progressively, the initially stationary lattices destabilize into coherent oscillatory motion. Our analysis yields both the instability threshold and the wavevector and polarization of the most unstable vibrational mode. The non-Markovian nature of the droplet dynamics renders the stability analysis of the hydrodynamic lattice more rich and subtle than that of its solid state counterpart.