2022 ESA Annual Meeting (August 14 - 19)

PS 33-148 Tailored vines and Taylor's Law: examining vine growth on Puerto Rican coffee farms

5:00 PM-6:30 PM
ESA Exhibit Hall
Simone Oliphant, University of Michigan;John Vandermeer,University of Michigan;
Background/Question/Methods

How to deal with weeds is one of the most persistent problems in modern agriculture. The goal of agroecologists generally, is not only to understand agricultural systems in an ecological context, but also to apply ecological principles in aiding farmers with potential problems, such as weeds. One such agricultural system is the coffee sector of Puerto Rico and the most evident weed problem is vines. We use simple ecological principles to understand the dynamics of vines that plague these coffee farms in Puerto Rico. The ecological tool we chose to use to this end is Taylor’s Law (TL). Discovered by L.R. Taylor in the 1960s, TL states that there is a power law relationship between a population’s size and its variance. Since its publication, L.R. Taylor’s paper has spawned countless studies in various fields including ecology. We use TL in an attempt to understand the evident variability of vines on coffee farms. To do this, over a period of 12 months from August 2018 to July 2019, vine coverage on 20 coffee plants in 26 different coffee farms was sampled.

Results/Conclusions

We found that not only are both the temporal and spatial forms of TL present on these coffee farms, but that the Lewontin-Cohen model of stochasticity (LC) was also at play within this study system. The LC model postulates that a population’s exponential rate of increase varies at random, independently of both the population’s size and time. The combination of Taylor’s Law and the Lewontin-Cohen model combine to explain both the general power law relationship between mean and variance and the deviation from the expectation of 1.0 for the parameter of that relationship. With these results, we hope that simple ecological laws will be able to help with weed management in agricultural systems.