American Society of Naturalists

A membership society whose goal is to advance and to diffuse knowledge of organic evolution and other broad biological principles so as to enhance the conceptual unification of the biological sciences.

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

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Silas B. Tittes, Joseph F. Walker, Lorena Torres-Martínez, and Nancy C. Emery (Apr 2019)

<i>Lasthenia ferrisiae</i>.<br />(Credit: Nancy C. Emery)
Lasthenia ferrisiae.
(Credit: Nancy C. Emery)

Do organisms live in environments where they do best, or are they simply persisting in environments they can tolerate? Discovering why organisms live where they do has motivated biologists for centuries and continues to be a major area of research today.

In a paper appearing in The American Naturalist, Tittes et al. examined the processes that determine where different species of the annual wildflower group called Goldfields (genus Lasthenia) grow across flooding gradients in seasonal wetlands called vernal pools. Despite being very closely related to one another, having almost identical life cycles, and growing within meters of one another in the field, different taxa occupy different microhabitats across vernal pool flooding gradients: some are always found in deeper positions within pools, while others occur at intermediate depths, and still others occur only in the uplands. The authors sought to understand if their positions in vernal pool landscapes could be predicted strictly by their performance in response to water levels. To do this, Tittes et al. developed a new method to compare how different organisms perform across environmental gradients. They used this method to quantify the performances of 14 Goldfield taxa that were raised under controlled water treatments that ranged from extreme drought to extended flooding. Finally, they evaluated if each taxon performed best under the conditions that matched those of its natural habitat.

Surprisingly, they found that all taxa had remarkably similar responses to flooding and drought, and performed best when grown in saturated soil without flooding. Thus, even though different taxa occupy microhabitats with very different water conditions, their responses to water alone do not determine where they occur in vernal pool landscapes. Instead, plant size was the best predictor of the places the different taxa live, perhaps because larger plants are more capable of competing in the environments that all taxa would find optimal.


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 (