Mon, Aug 15, 2022: 1:45 PM-2:00 PM
515C
Background/Question/MethodsAnimals can strongly impact biogeochemical cycles. Long term exclosure studies have demonstrated strong net effects of large mammalian herbivores (LMH) on ecosystem carbon and nitrogen cycling. However, the direction and magnitude of LMH effects are highly context dependent, particularly with respect to impacts on soil functioning. For instance, LMH effects on soil functioning can vary linearly or non-linearly across environmental gradients in precipitation, soil fertility, primary productivity, and disturbance history. Still, some recent studies have reported LMH impacts on vegetation with no clear impacts on soil-mediated biogeochemical cycling. A lack of general understanding of how LMH impacts on biogeochemical cycling vary in space and time is a key barrier to the field’s utility in developing whole-ecosystem management strategies and natural solutions to climate change. To address this urgent gap in our understanding, there is growing recognition for the need to (1) disentangle the relative contributions of plant consumption, physical trampling, and urination/defecation to the net effects of LMH on ecosystem functioning, and (2) examine effects across environmental contexts, but doing so represents a substantial challenge. This challenge has been underlined in recent reviews, which call for theory to help clarify the likely mechanisms by which LMH influence biogeochemical cycling.
Results/ConclusionsMathematical models are useful for disentangling alternative mechanisms underlying complex systems and can help guide empirical studies. In this talk, we present insights from a mathematical model of herbivore trampling effects on ecosystem nitrogen cycling. The model tracks nitrogen through a simplified ecosystem with both biotic (plants and detritivores) and abiotic (soil inorganic nitrogen and dead organic matter) nitrogen pools. We explore context-dependent effects of herbivore trampling on soil functioning. With a combination of analytical (partial derivatives) and graphical analyses, we discover that herbivore trampling can cause an increase, decrease, or no change in net nitrogen recycling rates. Which scenario occurs depends on the relative sensitivity of two detritivore-related pathways; intrinsic nutrient recycling rate and intrinsic mortality rate, to trampling. Further, depending on which functional pathway is most affected, detritivore biomass and net nitrogen recycling can be coupled or uncoupled. Thus, an important part of understanding trampling effects across environmental contexts is knowing which aspects of detritivore functioning (i.e. which pathways of nitrogen recycling) are more sensitive to trampling. Our model provides testable predictions to guide future progress in empirical and theoretical studies.
Results/ConclusionsMathematical models are useful for disentangling alternative mechanisms underlying complex systems and can help guide empirical studies. In this talk, we present insights from a mathematical model of herbivore trampling effects on ecosystem nitrogen cycling. The model tracks nitrogen through a simplified ecosystem with both biotic (plants and detritivores) and abiotic (soil inorganic nitrogen and dead organic matter) nitrogen pools. We explore context-dependent effects of herbivore trampling on soil functioning. With a combination of analytical (partial derivatives) and graphical analyses, we discover that herbivore trampling can cause an increase, decrease, or no change in net nitrogen recycling rates. Which scenario occurs depends on the relative sensitivity of two detritivore-related pathways; intrinsic nutrient recycling rate and intrinsic mortality rate, to trampling. Further, depending on which functional pathway is most affected, detritivore biomass and net nitrogen recycling can be coupled or uncoupled. Thus, an important part of understanding trampling effects across environmental contexts is knowing which aspects of detritivore functioning (i.e. which pathways of nitrogen recycling) are more sensitive to trampling. Our model provides testable predictions to guide future progress in empirical and theoretical studies.