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Abstract PremiseUnderstanding relationships among grass traits, fire, and herbivores may help improve conservation strategies for savannas that are threatened by novel disturbance regimes. Emerging theory, developed in Africa, emphasizes that functional traits of savanna grasses reflect the distinct ways that fire and grazers consume biomass. Specifically, functional trade‐offs related to flammability and palatability predict that highly flammable grass species will be unpalatable, while highly palatable species will impede fire. MethodsWe quantified six culm and leaf traits of 337 native grasses of Texas—a historical savanna region that has been transformed by fire exclusion, megafaunal extinctions, and domestic livestock. ResultsMultivariate analyses of traits revealed three functional strategies. “Grazer grasses” (N = 50) had culms that were short, narrow, and horizontal, and leaves with high width to length (W:L) and low C to N ratios (C:N)—trait values that attract grazers and avoid fire. “Fire grasses” (N = 104) had culms that were tall, thick, and upright, and leaves that were thick, with low W:L, and high C:N—trait values that promote fire and discourage grazers. “Generalist tolerators” and “generalist avoiders” (N = 183) had trait values that were intermediate to the other groups. ConclusionsOur findings confirm that the flammability–palatability trade‐offs that operate in Africa also explain correlated suites of traits in Texas grasses and highlights that the grass flora of Texas bears the signature of Pleistocene megafauna and the influence of fires that predate human arrival. We suggest that grass functional classifications based on fire and grazer traits can improve prescribed fire and livestock management of savannas of Texas and globally.
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On Building Modular and Elastic Data Structures with Bulk Operations
This paper introduces MEDS, a modular and elastic framework that simplifies the development of high-performance concurrent data structures that support linearizable primitive (i.e., add, remove, contains) and bulk (e.g., range query) operations.
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- PAR ID:
- 10232380
- Date Published:
- Journal Name:
- International Conference on Distributed Computing and Networking 2021 (ICDCN '21)
- Page Range / eLocation ID:
- 237 - 238
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3427796.3433932
