Performance curves are valuable tools for quantifying the fundamental niches of organisms and testing hypotheses about evolution, life-history trade-offs, and the drivers of variation in species’ distribution patterns. Here, we present a novel Bayesian method for characterizing performance curves that facilitates comparisons among species. We then use this model to quantify and compare the hydrological performance curves of 14 different taxa in the genus Lasthenia, an ecologically diverse clade of plants that collectively occupy a variety of habitats with unique hydrological features, including seasonally flooded wetlands called vernal pools. We conducted a growth chamber experiment to measure each taxon’s fitness across five hydrological treatments that ranged from severe drought to extended flooding, and we identified differences in hydrological performance curves that explain their associations with vernal pool and terrestrial habitats. Our analysis revealed that the distribution of vernal pool taxa in the field does not reflect their optimal hydrological environments: all taxa, regardless of habitat affinity, have highest fitness under similar hydrological conditions of saturated soil without submergence. We also found that a taxon’s relative position across flood gradients within vernal pools is best predicted by the height of its performance curve. These results demonstrate the utility of our approach for generating insights into when and how performance curves evolve among taxa as they diversify into distinct environments. To facilitate its use, the modeling framework has been developed into an R package.
Grow Where You Thrive, or Where Only You Can Survive? An Analysis of Performance Curve Evolution in a Clade with Diverse Habitat Affinities
Silas B. Tittes,Joseph F. Walker,L. Torres-Martínez,N. Emery
Published 2019 in American Naturalist
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- Publication year
2019
- Venue
American Naturalist
- Publication date
2019-02-18
- Fields of study
Biology, Medicine, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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