Wolfgang Traylor , Matthew Forrest, Thomas Hickler

During the last glacial, large grazers inhabited Eurasia’s mammoth steppe. This cold steppe was productive enough to sustain a diverse assemblage of large mammals, but it remains controversial which population densities it could support. In sufficient densities, large herbivores can act as ecosystem engineers: creating and maintaining grassland habitat by means of disturbance and accelerated nutrient cycling. Estimating carrying capacity (i.e., long-term mean megafauna densities) of the Pleistocene mammoth steppe is therefore crucial for understanding this paleoecosystem. In this study, we developed a process-based grazer model, dynamically coupled with a dynamic global vegetation model, in order to simulate a range of plausible glacial grazer densities. In order to capture parameter uncertainty we defined prior probability/mass distributions from literature for all grazer parameters. We parameterized and simulated the woolly mammoth (Mammuthus primigenius) because it is the largest and metabolically most efficient megafaunal species; therefore its per-area mass densities mark an upper bound for whole-guild carrying capacity. We sampled the parameter space with Monte-Carlo Markov Chains to derive parameter sensitivity and a posterior probability distribution of megafauna densities. In absence of mammoth densities for model fitting we chose a likelihood function that maximizes mammoth survival over its climatic niche. This approach let densities emerge bottom up from the mechanistic model. Our results identify 4% annual mortality of adults as an upper limit to survival of mammoth populations, which corroborates their high vulnerability to human hunting. The resulting posterior densities for mammoth steppe carrying capacity range from 13 to 85 kg/ha (95% quantile), which lies in between lower and higher estimates from other publications. We discuss reasons why our results should be interpreted as an upper limit to mammoth steppe carrying capacity. Even though our approach could only capture part of the predictive uncertainty, our results prompt caution to extrapolate very high potential megafauna densities to global scale as a natural baseline.