Mon, Aug 15, 2022: 4:00 PM-4:15 PM
515C
Background/Question/MethodsThe statistical distribution of herbivory patterns, particularly the level of variability, has long caught the interest of plant ecologists. These patterns of herbivory are often used as evidence for a wide array of biological phenomena, including differences in plant defensive chemistry, plant induced resistance, and herbivore feeding behavior. The field, however, still lacks a first principle null expectation for herbivory distributions, and this limits our ability to distinguish between herbivory patterns caused by plant biology and purely neutral stochastic processes. Accordingly, we propose a neutral model based on metabolic theory that describes the statistical distribution of herbivory caused by randomly feeding herbivores on identical, passive plants. We generate quantitative and qualitative predictions from the model, focusing on the variation of herbivory at the within and among individual scale. We also generate neutral predictions for the effect of key plant traits and environmental factors, including leaf life span, plant size, season, and herbivore density. To identify herbivory patterns of biological interest, we compared our predictions against > 400k records of global field survey data on herbivory from the Herbivory Variability Network.
Results/ConclusionsSurprisingly, our neutral model using only one free parameter (i.e. mean herbivory) is able to replicate the observed frequency distribution of herbivory at the among individual leaf level very well (402 plant species across 616 surveys). Zooming out to the among individual plant scale, observed herbivory patterns show more deviations from the neutral predictions, a moderate portion of which is explained by phylogenetic relatedness. Because herbivory accumulates on plants as a repeated additive process, we show that herbivory asymptotically approaches a normal distribution under most circumstances. As a result, and consistent with empirical patterns, the variability of herbivory distribution shows a characteristic decline with increasing duration of herbivore exposure and herbivore density. Our neutral model also successfully predicts the qualitative, but not quantitative, negative relationship between herbivory variability and leaf life span and plant size. Taken together, our result suggests that qualitative herbivory patterns can be largely described by neutral processes, but non-neutral biological phenomena nevertheless are needed to fully explain quantitative patterns. Moving forward, more careful consideration of the null expectations and quantitative test of patterns are necessary to make biological inferences from herbivory patterns.
Results/ConclusionsSurprisingly, our neutral model using only one free parameter (i.e. mean herbivory) is able to replicate the observed frequency distribution of herbivory at the among individual leaf level very well (402 plant species across 616 surveys). Zooming out to the among individual plant scale, observed herbivory patterns show more deviations from the neutral predictions, a moderate portion of which is explained by phylogenetic relatedness. Because herbivory accumulates on plants as a repeated additive process, we show that herbivory asymptotically approaches a normal distribution under most circumstances. As a result, and consistent with empirical patterns, the variability of herbivory distribution shows a characteristic decline with increasing duration of herbivore exposure and herbivore density. Our neutral model also successfully predicts the qualitative, but not quantitative, negative relationship between herbivory variability and leaf life span and plant size. Taken together, our result suggests that qualitative herbivory patterns can be largely described by neutral processes, but non-neutral biological phenomena nevertheless are needed to fully explain quantitative patterns. Moving forward, more careful consideration of the null expectations and quantitative test of patterns are necessary to make biological inferences from herbivory patterns.