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Factors controlling mosses on the forest floor in western North America are poorly understood. We examined elevational distributions for six of the most abundant large forest floor mosses; based on those distributions, a transplant experiment of two species evaluated if interspecific interactions can be mediated by climatic context. Mosses had species-specific elevational profiles, with Rhytidiopsis robusta more prominent at higher elevations, while Hylocomium splendens, Kindbergia oregana, Rhytidiadelphus loreus, and Rhytidiadelphus triquetrus were more prominent at lower elevations. Homalothecium megaptilum was bimodal, peaking at middle and low elevations. We selected Rhytidiadelphus triquetrus and Rhytidiopsis robusta for a transplant experiment because each is prominent at different elevations and they are similar in stature. Moss mat squares cut from the forest floor at middle elevations were transplanted in a single- or mixed-species pattern at two sites, one high elevation and one low elevation. We recorded changes in percent cover within the squares over one year as well as outgrowth onto bare soil and litter. Hypothesized relative species performances based on elevational distributions were mostly not supported. The low-elevation associated species (Rhytidiadelphus triquetrus) outperformed the high-elevation species (Rhytidiopsis robusta) at the high-elevation site, both in a mixture and as a monoculture. At the lower site, Rhytidiadelphus triquetrus grew well in a mixture, but the monoculture declined. Furthermore, Rhytidiopsis robusta grew faster at low elevation than at high, both in a mixture and monoculture, despite being more abundant at high elevations. Poor performance of both species at high elevations raises interesting questions about what factors limit moss mats in general at higher elevations in the Cascade Range. 

Abstract The biodiversity crisis necessitates spatially extensive methods to monitor multiple taxonomic groups for evidence of change in response to evolving environmental conditions. Programs that combine passive acoustic monitoring and machine learning are increasingly used to meet this need. These methods require large, annotated datasets, which are time‐consuming and expensive to produce, creating potential barriers to adoption in data‐ and funding‐poor regions. Recently released pre‐trained avian acoustic classification models provide opportunities to reduce the need for manual labelling and accelerate the development of new acoustic classification algorithms through transfer learning. Transfer learning is a strategy for developing algorithms under data scarcity that uses pre‐trained models from related tasks to adapt to new tasks.Our primary objective was to develop a transfer learning strategy using the feature embeddings of a pre‐trained avian classification model to train custom acoustic classification models in data‐scarce contexts. We used three annotated avian acoustic datasets to test whether transfer learning and soundscape simulation‐based data augmentation could substantially reduce the annotated training data necessary to develop performant custom acoustic classifiers. We also conducted a sensitivity analysis for hyperparameter choice and model architecture. We then assessed the generalizability of our strategy to increasingly novel non‐avian classification tasks.With as few as two training examples per class, our soundscape simulation data augmentation approach consistently yielded new classifiers with improved performance relative to the pre‐trained classification model and transfer learning classifiers trained with other augmentation approaches. Performance increases were evident for three avian test datasets, including single‐class and multi‐label contexts. We observed that the relative performance among our data augmentation approaches varied for the avian datasets and nearly converged for one dataset when we included more training examples.We demonstrate an efficient approach to developing new acoustic classifiers leveraging open‐source sound repositories and pre‐trained networks to reduce manual labelling. With very few examples, our soundscape simulation approach to data augmentation yielded classifiers with performance equivalent to those trained with many more examples, showing it is possible to reduce manual labelling while still achieving high‐performance classifiers and, in turn, expanding the potential for passive acoustic monitoring to address rising biodiversity monitoring needs.