Significance: Fossil evidence shows that Polynesians introduced the tropical crop taro (Colocasia esculenta) during initial colonization of the subtropical South Pacific islands and temperate New Zealand after 1200 CE, establishing garden ecosystems with similar commensal plants and invertebrates. Sedimentary charcoal and fossil remains indicate how frequent burning and perennial cultivation overcame the ecological constraints for taro production, particularly the temperate forest cover of New Zealand. An increase in short-lived plants, indicating a transition toward higher-intensity production, followed rapid woody forest decline and species extinctions on all islands. The relatively recent fossil records from the subtropical and temperate islands of Polynesia provide unique insights into the ecological processes behind the spread of Neolithic crops into areas marginal for production.

Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model based on an openly available PyTorch implementation of Faster R-CNN ResNet 50. Results indicate 100% agreement between the model and original human derived classifications, a better performance than the results achieved independently by two human analysts who reassessed the 2D images available to the machine learning model. Machine learning neural networks provide the potential to consistently assess the composition of large archaeological assemblages composed of objects modified in a variety of ways.