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Creators/Authors contains: "Atkins, David"

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  1. Abstract

    Plant populations are limited by resource availability and exhibit physiological trade‐offs in resource acquisition strategies. These trade‐offs may constrain the ability of populations to exhibit fast growth rates under water limitation and high cover of neighbours. However, traits that confer drought tolerance may also confer resistance to competition. It remains unclear how fitness responses to these abiotic conditions and biotic interactions combine to structure grassland communities and how this relationship may change along a gradient of water availability.

    To address these knowledge gaps, we estimated the low‐density growth rates of populations in drought conditions with low neighbour cover and in ambient conditions with average neighbour cover for 82 species in six grassland communities across the Central Plains and Southwestern United States. We assessed the relationship between population tolerance to drought and resistance to competition and determined if this relationship was consistent across a precipitation gradient. We also tested whether population growth rates could be predicted using plant functional traits.

    Across six sites, we observed a positive correlation between low‐density population growth rates in drought and in the presence of interspecific neighbours. This positive relationship was particularly strong in the grasslands of the northern Great Plains but weak in the most xeric grasslands. High leaf dry matter content and a low (more negative) leaf turgor loss point were associated with high population growth rates in drought and with neighbours in most grassland communities.

    Synthesis: A better understanding of how both biotic and abiotic factors impact population fitness provides valuable insights into how grasslands will respond to extreme drought. Our results advance plant strategy theory by suggesting that drought tolerance increases population resistance to interspecific competition in grassland communities. However, this relationship is not evident in the driest grasslands, where above‐ground competition is likely less important. Leaf dry matter content and turgor loss point may help predict which populations will establish and persist based on local water availability and neighbour cover, and these predictions can be used to guide the conservation and restoration of biodiversity in grasslands.

     
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  2. There are many different forms of psychotherapy. Itemized inventories of psychotherapeutic interventions provide a mechanism for evaluating the quality of care received by clients and for conducting research on how psychotherapy helps. However, evaluations such as these are slow, expensive, and are rarely used outside of well-funded research studies. Natural language processing research has progressed to allow automating such tasks. Yet, NLP work in this area has been restricted to evaluating a single approach to treatment, when prior research indicates therapists used a wide variety of interventions with their clients, often in the same session. In this paper, we frame this scenario as a multi-label classification task, and develop a group of models aimed at predicting a wide variety of therapist talk-turn level orientations. Our models achieve F1 macro scores of 0.5, with the class F1 ranging from 0.36 to 0.67. We present analyses which offer insights into the capability of such models to capture psychotherapy approaches, and which may complement human judgment. 
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  3. Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue. 
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