Publications by authors named "A E Clark"

Context: Historical land use is thought to have influenced plant community diversity, composition and function through the local persistence of taxa that reflect ecological conditions of the past.

Objectives: We tested for the effects of historical land use on contemporary plant species richness, composition, and ecological preferences in the grassland vegetation of Central Europe.

Methods: We analyzed 6975 vegetation plots sampled between 1946 and 2021 in dry, mesic, and wet grasslands in the borderland between Austria, the Czech Republic, and Slovakia.

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Our commentary suggests that different materialities (fragile, enduring, and mixed) may influence cognitive evolution. Building on Stibbard-Hawkes, we propose that predictive brains minimise errors and seek information, actively structuring environments for epistemic benefits. This perspective complements Stibbard-Hawkes' view.

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Background: Postoperative dressings expedite wound healing and decrease the rate of infection. Options for wound dressings vary based on cost, time to apply, method of wound healing, and availability at the hospital; however, a significant difference in postoperative complications between each type has not been found. As such, this study evaluates patient cosmetic preferences for various wound dressings as it relates to early postoperative satisfaction.

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Background: Postpartum is a critical period to interrupt weight gain across the lifespan, decrease weight-related risk in future pregnancies, promote healthy behaviors that are often adopted during pregnancy, and improve long-term health. Because the postpartum period is marked by unique challenges to a person's ability to prioritize healthy behaviors, a multi-level/domain approach to intervention beyond the individual-level factors of diet and activity is needed.

Objectives: The purpose of this study was to understand postpartum people's perceptions about the relationship between their social networks and support, and their health behaviors and weight.

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Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents' self-reported wellbeing.

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