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Abstract Palaeontological field surveys in remote regions are a challenge, because of uncertainty in finding new specimens, high transportation costs, risks for the crew and a long time commitment. The effort can be facilitated by using high-resolution satellite imagery. Here we present a new opportunity to investigate remote fossil localities in detail, mapping the optical signature of individual fossils. We explain a practical workflow for detecting fossils using remote-sensing platforms and cluster algorithms. We tested the method within the Petrified Forest National Park, where fossil logs are sparse in a large area with mixed lithologies. We ran both unsupervised and supervised classifications, obtaining the best estimations for the presence of fossil logs using the likelihood and spectral angle mapper algorithms. We recognized general constraints and described logical and physical pros and cons of each estimated map. We also explained how the outcomes should be critically evaluated with consistent accuracy tests. Instead of searching for fossiliferous outcrops, our method targets single fossil specimens (or highly condensed accumulations), obtaining a significant increase in potential efficiency and effectiveness of field surveys. When repeatedly applied to the same region over time, it could also be useful for monitoring palaeontological heritage localities. Most importantly, the method here described is feasible, easily applicable to both fossil logs and bones, and represents a step towards standard best practices for applying remote sensing in the palaeontological field. 

Abstract Natural history collections are repositories of biodiversity specimens that provide critical infrastructure for studies of mammals. Over the past 3 decades, digitization of collections has opened up the temporal and spatial properties of specimens, stimulating new data sharing, use, and training across the biodiversity sciences. These digital records are the cornerstones of an “extended specimen network,” in which the diverse data derived from specimens become digital, linked, and openly accessible for science and policy. However, still missing from most digital occurrences of mammals are their morphological, reproductive, and life-history traits. Unlocking this information will advance mammalogy, establish richer faunal baselines in an era of rapid environmental change, and contextualize other types of specimen-derived information toward new knowledge and discovery. Here, we present the Ranges Digitization Network (Ranges), a community effort to digitize specimen-level traits from all terrestrial mammals of western North America, append them to digital records, publish them openly in community repositories, and make them interoperable with complimentary data streams. Ranges is a consortium of 23 institutions with an initial focus on non-marine mammal species (both native and introduced) occurring in western Canada, the western United States, and Mexico. The project will establish trait data standards and informatics workflows that can be extended to other regions, taxa, and traits. Reconnecting mammalogists, museum professionals, and researchers for a new era of collections digitization will catalyze advances in mammalogy and create a community-curated trait resource for training and engagement with global conservation initiatives. 

The cause, or causes, of the Pleistocene megafaunal extinctions have been difficult to establish, in part because poor spatiotemporal resolution in the fossil record hinders alignment of species disappearances with archeological and environmental data. We obtained 172 new radiocarbon dates on megafauna from Rancho La Brea in California spanning 15.6 to 10.0 thousand calendar years before present (ka). Seven species of extinct megafauna disappeared by 12.9 ka, before the onset of the Younger Dryas. Comparison with high-resolution regional datasets revealed that these disappearances coincided with an ecological state shift that followed aridification and vegetation changes during the Bølling-Allerød (14.69 to 12.89 ka). Time-series modeling implicates large-scale fires as the primary cause of the extirpations, and the catalyst of this state shift may have been mounting human impacts in a drying, warming, and increasingly fire-prone ecosystem.