http://hdl.handle.net/11122/1003 Tue, 17 Mar 2026 00:42:04 GMT 2026-03-17T00:42:04Z http://hdl.handle.net/11122/16266 Data Submission Package for MS Svalvard LEX Surveys with ML/AI analysis (pending review) Huettmann, Falk This dataset consists of the Appendices used for the analysis of Svalbard data seabirds and marine mammals. Field data and GBIF.org data and OBIS data and PIROP data for seabirds and marine mammals Wed, 12 Nov 2025 00:00:00 GMT http://hdl.handle.net/11122/16266 2025-11-12T00:00:00Z http://hdl.handle.net/11122/16263 Data for Manuscript (MS; in review) Common Leopard and Snow Leopard Overlap Models in Central Himalaya, Nepal Ale, Purna Bahadur; Huettmann, Falk Data used for a manuscript (MS) on Common Leopard and Snow Leopard habitat overlap models in the Central Himalayas during the Anthropocene. This data set is used for habitat overlaps model predictions of Common Leopard and Snow Leopards in the Central Himalaya, Nepal using camera trap data.. Mon, 10 Nov 2025 00:00:00 GMT http://hdl.handle.net/11122/16263 2025-11-10T00:00:00Z http://hdl.handle.net/11122/16238 Data for "What are the various roles and perceptions of squirrels in Alaska Native cultures? A proof-of-concept using Machine Learning with insights from interviews with Alaska Native communities" Steiner, Moriz Alaska Native communities have lived in Alaska for over 15,000 years, developing sustainable subsistence activities and a close relationship with the surrounding landscape, flora, and fauna. In this study, I explored the role(s) squirrels play in this relationship across different Alaska Native communities in interior and western Alaska. I carried out six interviews with Alaska Native ‘users’ and trappers/ hunters to assess their perception of squirrel fur values, hunting interests, spiritual links, and as food. To describe and analyze the interviews and underlying signals therein, I use a novel Machine Learning framework with two algorithms (CART (Classification and Regression Trees), and TreeNet gradient boosting) to present differences between the role of squirrels in users and trappers/hunters, as well as across different communities. Thereby, I also aim to detect the strongest signals in the data that are otherwise likely missed when following conventional interview analysis methods (manifest and latent content analyses). With this interdisciplinary approach, I provide a proof-of-concept of this synergized approach for progress in combining natural science data mining and social sciences by including several guiding rules of thumb to facilitate interviews among Alaskan Native communities. These guidelines provide insights into the lessons learned from this interdisciplinary approach and include suggested future approaches for a more complete data gathering process, while remaining achievable within academic and higher education student timelines. Fri, 31 Oct 2025 00:00:00 GMT http://hdl.handle.net/11122/16238 2025-10-31T00:00:00Z http://hdl.handle.net/11122/16237 Data for "Heavy Metal and Essential Elements Analyses of North American Red Squirrels in Interior Alaska: First data mining implications in squirrels as sentinel species with a One-Health outlook" Steiner, Moriz Exceedingly high or low concentrations of essential elements and heavy metals can have devastating effects on homeostasis and the health of living organisms. Baseline ranges for many elements have been widely assessed and studied for domesticated species, but such a baseline is lacking for squirrels, especially in remote areas such as Alaska (United States). North American red squirrels (Tamiasciurus hudsonicus) are mesopredators that interact with a vast array of ecosystem components (fungi, seeds and vegetation, predators, and species that co-evolved with squirrels), while being able to thrive in semi-urbanized landscapes of the Anthropocene. As such, they can serve as a sentinel species for ecosystem health. To assess baseline heavy metal exposure and essential element concentrations in Interior Alaskan red squirrels, we collected 158 squirrels through citizen crowdsourcing and extracted livers from each squirrel during necropsies. We grouped the 158 squirrels into 11 composite samples (based on the location of the deceased animal) and analyzed liver tissues for 71 elements using Inductively Coupled Plasma-Sector Field Mass Spectrometry (ICP-MS). Our laboratory results mostly fall within the range reported in the few available literature records for liver tissue analyzed within the Sciuridae family, but tend to be found in the highest quarter of comparable literature records for manganese (Mn), cadmium (Cd), mercury (Hg), nickel (Ni), and lead (Pb). Of the total 71 elements measured, we analyzed 21 using the TreeNet Gradient Boosting Machine Learning algorithm in the Salford Predictive Modeler (SPM) and present three elements (As, Se, and Cd) as case studies. These 21 elements were chosen based on our judgment of their relevance for the health of red squirrels in the study area. We used 224 environmental predictors and ‘scored’ the predictions in SPM, linking the aspatial TreeNet models to the corresponding spatial location. We then generate a predicted geo-referenced element concentration value (data point) for each pixel for each assessed element. Hence, the exploratory spatial predictions enabled us to increase the original composite sample size of 11 sites to a much larger Alaskan landscape (approximately 656,000 predicted sites), leading us to the territory of Big Data approaches, representing a core novelty aspect of this study. Some of the major heavy metal prediction hotspots were found to be near active and inactive military sites (e.g., Murphy Dome and Salcha sites in Interior Alaska for Se, Cd, and As), raising potential concerns for heavy metal contaminations of nearby landscapes. This approach can be further utilized as a planning tool for future on-the-ground sampling of squirrels or other components of the landscape. Fri, 31 Oct 2025 00:00:00 GMT http://hdl.handle.net/11122/16237 2025-10-31T00:00:00Z