Predicting drought tolerance from slope aspect preference in restored plant communities
Kimball, Sarah; Lulow, Megan; Balazs, Kathleen; Huxman, Travis (2017), Predicting drought tolerance from slope aspect preference in restored plant communities, Dataset, https://doi.org/10.7280/D1JD4X
Plants employ strategies of tolerance, endurance, and avoidance to cope with aridity in space and time, yet understanding the differential importance of such strategies in determining patterns of abundance across a heterogeneous landscape is a challenge. Are the species abundant in drier microhabitats also better able to survive drought? Are there relationships among occupied sites and temporal dynamics that derive from physiological capacities to cope with stress or dormancy during unfavorable periods? We used a restoration project conducted on two slope aspects in a subwatershed to test whether species that were more abundant on more water-limited S-facing slopes were also better able to survive an extreme drought. The attempt to place many species uniformly on different slope aspects provided an excellent opportunity to test questions of growth strategy, niche preference, and temporal dynamics. Perennial species that established and grew best on S-facing slopes also had greater increases in cover during years of drought, presumably by employing drought tolerance and endurance techniques. The opposite pattern emerged for annual species that employed drought-escape strategies, such that annuals that occupied S-facing slopes were less abundant during the drought than those that were more abundant on N-facing slopes. Our results clarify how different functional strategies interact with spatial and temporal heterogeneity to influence population and community dynamics and demonstrate how large restoration projects provide opportunities to test fundamental ecological questions.
Each May (late Spring at our study site), from 2012 to 2015, we used point-intercept, with 24 points per 5 × 5 m plot, to determine the percent cover of all species.
- This dataset is part of https://doi.org/10.1002/ece3.2881