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Effect of stressors on the carrying capacity of spatially distributed metapopulations

Citation

Zhang, Bo et al. (2021), Effect of stressors on the carrying capacity of spatially distributed metapopulations, Dryad, Dataset, https://doi.org/10.25338/B8090K

Abstract

Stressors such as antibiotics, herbicides and pollutants are becoming increasingly common in the environment. The effects of stressors on populations are typically studied in homogeneous, non-spatial settings. However, most populations in nature are spatially distributed over environmentally heterogeneous landscapes with spatially-restricted dispersal. Little is known about the effects of stressors in these more realistic settings. Here, we combine laboratory experiments with novel mathematical theory to rigorously investigate how a stressor’s physiological effect and spatial distribution interact with dispersal to influence population dynamics. We prove mathematically that if a stressor increases death rate and/or simultaneously decreases population growth rate and yield, a homogeneous distribution of stressor leads to a lower total population size than if the same amount of stressor was heterogeneously distributed. We experimentally test this prediction on spatially-distributed populations of budding yeast, Saccharomyces cerevisiae . We find that the antibiotic, cycloheximide, increases yeast death rate but reduces growth rate and yield. Consistent with our mathematical predictions, we observe that a homogeneous spatial distribution of cycloheximide minimizes the total equilibrium size of experimental metapopulations, with the magnitude of the effect depending predictably on dispersal rate and geographic pattern of antibiotic heterogeneity. Our study has implications for assessing population risk posed by pollutants, antibiotics, and global change, and in the rational design of strategies for employing toxins to control pathogens and pests.

Methods

The data were collected with budding yeast. We measured population density using a microplate photometer (Tecan Infinite M200 Pro) to obtain the optical density at 660 nm (OD660) of each well.

Simulations were performed in Matlab R2018b.