The North American tree-ring fire-scar network

Fire regimes in North American forests are diverse and modern fire records are often too short to capture important patterns, trends, feedbacks, and drivers of variability. Tree-ring fire scars provide valuable perspectives on fire regimes, including centuries-long records of fire year, season, frequency, severity, and size. Here, we introduce the newly compiled North American tree-ring fire-scar network (NAFSN), which contains 2562 sites, >37,000 fire-scarred trees, and covers large parts of North America. We investigate the NAFSN in terms of geography, sample depth, vegetation, topography, climate, and human land use. Fire scars are found in most ecoregions, from boreal forests in northern Alaska and Canada to subtropical forests in southern Florida and Mexico. The network includes 91 tree species, but is dominated by gymnosperms in the genus Pinus. Fire scars are found from sea level to >4000-m elevation and across a range of topographic settings that vary by ecoregion. Multiple regions are densely sampled (e.g., >1000 fire-scarred trees), enabling new spatial analyses such as reconstructions of area burned. To demonstrate the potential of the network, we compared the climate space of the NAFSN to those of modern fires and forests; the NAFSN spans a climate space largely representative of the forested areas in North America, with notable gaps in warmer tropical climates. Modern fires are burning in similar climate spaces as historical fires, but disproportionately in warmer regions compared to the historical record, possibly related to under-sampling of warm subtropical forests or supporting observations of changing fire regimes. The historical influence of Indigenous and non-Indigenous human land use on fire regimes varies in space and time. A 20th century fire deficit associated with human activities is evident in many regions, yet fire regimes characterized by frequent surface fires are still active in some areas (e.g., Mexico and the southeastern United States). These analyses provide a foundation and framework for future studies using the hundreds of thousands of annually- to sub-annually-resolved tree-ring records of fire spanning centuries, which will further advance our understanding of the interactions among fire, climate, topography, vegetation, and humans across North America

Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire : an expert assessment

As the permafrost region warms, its large organic carbon pool will be increasingly vulnerable to decomposition, combustion, and hydrologic export. Models predict that some portion of this release will be offset by increased production of Arctic and boreal biomass; however, the lack of robust estimates of net carbon balance increases the risk of further overshooting international emissions targets. Precise empirical or model-based assessments of the critical factors driving carbon balance are unlikely in the near future, so to address this gap, we present estimates from 98 permafrost-region experts of the response of biomass, wildfire, and hydrologic carbon flux to climate change. Results suggest that contrary to model projections, total permafrost-region biomass could decrease due to water stress and disturbance, factors that are not adequately incorporated in current models. Assessments indicate that end-of-the-century organic carbon release from Arctic rivers and collapsing coastlines could increase by 75% while carbon loss via burning could increase four-fold. Experts identified water balance, shifts in vegetation community, and permafrost degradation as the key sources of uncertainty in predicting future system response. In combination with previous findings, results suggest the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario but that 65%-85% of permafrost carbon release can still be avoided if human emissions are actively reduced.Peer reviewe

Hierarchical Bayesian Models of Verb Learning in Children

The productivity of language lies in the ability to generalize linguistic knowledge to new situations. To understand how children can learn to use language in novel, productive ways, we must investigate how children can find the right abstractions over their input, and how these abstractions can actually guide generalization. In this thesis, I present a series of hierarchical Bayesian models that provide an explicit computational account of how children can acquire and generalize highly abstract knowledge of the verb lexicon from the language around them. By applying the models to large, naturalistic corpora of child-directed speech, I show that these models capture key behaviours in child language development. These models offer the power to investigate developmental phenomena with a degree of breadth and realism unavailable in existing computational accounts of verb learning. By most accounts, children rely on strong regularities between form and meaning to help them acquire abstract verb knowledge. Using a token-level clustering model, I show that by attending to simple syntactic features of potential verb arguments in the input, children can acquire abstract representations of verb argument structure that can reasonably distinguish the senses of a highly polysemous verb. I develop a novel hierarchical model that acquires probabilistic representations of verb argument structure, while also acquiring classes of verbs with similar overall patterns of usage. In a simulation of verb learning within a broad, naturalistic context, I show how this abstract, probabilistic knowledge of alternations can be generalized to new verbs to support learning. I augment this verb class model to acquire associations between form and meaning in verb argument structure, and to generalize this knowledge appropriately via the syntactic and semantic aspects of verb alternations. The model captures children's ability to use the alternation pattern of a novel verb to infer aspects of the verb's meaning, and to use the meaning of a novel verb to predict the range of syntactic forms in which the verb may participate. These simulations also provide new predictions of children's linguistic development, emphasizing the value of this model as a useful framework to investigate verb learning in a complex linguistic environment.Ph

Generalizing between form and meaning using learned verb classes

Early language development critically depends on the ability to form abstract representations of linguistic knowledge, and to generalize that knowledge to new situations. In verb knowledge, much generalization appears to be driven by various regularities between form and meaning, but it is difficult to assess how these factors interact in a complex learning environment. knowledge of verbs from naturalistic child-directed speech, then generalize these abstractions to novel verbs, simulating child behaviour. We use the syntactic alternation structure of a novel verb to infer aspects of its meaning, and use the meaning of a novel verb to predict its range of acceptable syntactic forms. The model provides a useful framework to investigate the interaction of complex factors in verb learning

An incremental bayesian model for learning syntactic categories

We present an incremental Bayesian model for the unsupervised learning of syntactic categories from raw text. The model draws information from the distributional cues of words within an utterance, while explicitly bootstrapping its development on its own partiallylearned knowledge of syntactic categories. Testing our model on actual child-directed data, we demonstrate that it is robust to noise, learns reasonable categories, manages lexical ambiguity, and in general shows learning behaviours similar to those observed in children.