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We investigate the roles of linguistic and sensory experience in the early-produced visual, auditory, and abstract words of congenitally-blind toddlers, deaf toddlers, and typicallysighted/ hearing peers. We also assess the role of language access by comparing early word production in children learning English or American Sign Language (ASL) from birth, versus at a delay. Using parental report data on child word production from the MacArthur-Bates Communicative Development Inventory, we found evidence that while children produced words referring to imperceptible referents before age 2, such words were less likely to be produced relative to words with perceptible referents. For instance, blind (vs. sighted) children said fewer highly visual words like “blue” or “see”; deaf signing (vs. hearing) children produced fewer auditory signs like HEAR. Additionally, in spoken English and ASL, children who received delayed language access were less likely to produce words overall. These results demonstrate and begin to quantify how linguistic and sensory access may influence which words young children produce. 

Global biodiversity is diminishing rapidly. The number of accepted “background” extinction rates range from 0.1 to 4 species per million per year. However, some authors have noted an increase in extinctions to 150–260 species per million per year since 1500. As the largely anthropogenic-induced global biodiversity crisis progresses, an abundance of taxa are faced with changing environments, decreasing population sizes, and/or extinction. Concerning amphibians, at least 43% of all species are experiencing population declines, with the 2008 Global Amphibian Assessment showing that 32% of all amphibian species are threatened or extinct. Populations of high-alpine amphibians in the southern Rocky Mountain region (i.e., Boreal toads [Anaxyrus boreas boreas], Tiger salamanders [Ambystoma mavortium], and Boreal chorus frogs [Pseudacris maculata]) have swiftly declined in recent decades due to high 649 susceptibility to diseases (e.g., Batrachochytrium dendrobatidis and ranaviruses) and changing environmental conditions. This study will focus on the effects of climate change by examining the habitat of these three amphibious species. A Maximum Entropy (MaxEnt) model is a commonly used machine-learning method of creating SDMs by accounting for the most uniform distribution and the provided predictors. Well-fitted models can be produced for different climatic scenarios by including climatic and environmental factors, or predictors, relevant to the species of interest, appropriately adjusting the regularization parameter and active feature classes for each model, and properly evaluating the models. By adopting this method of SDMs along with a decadal time-series approach, we demonstrate a larger-scale pattern of potential habitat shifts over time in this alpine habitat caused by climate change.