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Data from: Grow where you thrive, or where only you can survive? An analysis of performance curve evolution in a clade with diverse habitat affinities

Citation

Tittes, Silas B.; Walker, Joseph F.; Torres-Martinez, Lorena; Emery, Nancy C. (2018), Data from: Grow where you thrive, or where only you can survive? An analysis of performance curve evolution in a clade with diverse habitat affinities, Dryad, Dataset, https://doi.org/10.5061/dryad.vj134s0

Abstract

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 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 do 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 (https://github.com/silastittes/performr).

Usage Notes

Funding

National Science Foundation, Award: DEB-1354900 and DEB-1520052