SYMP 10-6 - Stoichiometric distribution models to investigate the role of consumer-resource interactions on the spatial variation in nutrient stocks at landscape extents

Wednesday, August 9, 2017: 10:40 AM
D135, Oregon Convention Center
Shawn J. Leroux, Eric Vander Wal, Yolanda Wiersma and Jonathan D. Ebel, Biology, Memorial University of Newfoundland, St. John's, NF, Canada

There is growing evidence that animals can play a critical role in ecosystem nutrient cycling. The processes of herbivory and predation can directly influence the distribution of nutrients among trophic levels which can indirectly influence the chemical composition of organic matter available for decomposition by microbes. Our current knowledge of the role of animals in nutrient cycling derives mostly from experiments conducted at small extents and therefore, we currently lack an understanding of how broad spatial patterns in animal distribution and interactions affect the geography of biogeochemicals. In this contribution we develop stoichiometric distribution models (StDMs), which allow us to evaluate the influence of consumer-resource interactions on the spatial structure of nutrient composition across a landscape. We parameterize StDMs for a moose-white birch consumer-resource system in Newfoundland, Canada. With this case study we assess i) how nutrient composition of food resources influences the spatial distribution of consumers and conversely, ii) how consumer browsing influences the spatial patterns in resource nutrient stocks across the landscape.


Knowledge of the spatial variation in nutrient composition of a key food resource (i.e., white birch) improved our predictions of the spatial distribution of moose. Specifically, model selection analysis showed that moose distribution models which included covariates for white birch nutrient ratios (e.g., C:N, C:P) consistently ranked higher than moose distribution models without these covariates. In addition, we provide evidence that white birch stoichiometric distribution models which include landscape level covariates such as terrain, canopy cover and land cover can explain a large proportion of the variation in white birch nutrient composition across the landscape. We argue that StDMs are a promising tool to understand the effects of animal distribution and interactions on the spatial variation in nutrient stocks at landscape extents. We end with perspectives for further development and application of StDMs to advance four emerging frameworks for spatial ecosystem ecology in an era of global change; meta-ecosystem theory, macroecological stoichiometry, remotely sensed biogeochemistry, and individual-based spatial nutritional ecology.