Nature's variability plays a major role in maintenance of biodiversity. As global change is altering variability, understanding how key food web structures maintain stability in the face of variation becomes critical. Surprisingly, little research has been undertaken to mechanistically understand how key food web structures are expected to operate in a noisy world and what this means for stability. Omnivory, for example, has been historically well studied but largely from a static perspective. Recent empirical evidence suggests that the strength of omnivory varies in response to changing conditions in ways that may be fundamental to stability. In the present article, we extend existing omnivory theory to predict how omnivory responds to variation and to show that dynamic omnivory responses are indeed a potent stabilizing structure in the face of variation. We end by synthesizing empirical examples within this framework, demonstrating the ubiquity of the theoretical mechanisms proposed across ecosystem types, spatial scales, and taxa.

No abstract

The fragmented ecosystems along the Niagara Escarpment World Biosphere Reserve provide important habitats for biota including lichens. Nonetheless, the Reserve is disturbed by dense human populations and associated air pollution. Here we investigated patterns of lichen diversity within urban and rural sites at three different locations (Niagara, Hamilton, and Owen Sound) along the Niagara Escarpment in Ontario, Canada. Our results indicate that both lichen species richness and community composition are negatively correlated with increasing human population density and air pollution. However, our quantitative analysis of community composition using canonical correspondence analysis (CCA) indicates that human population density and air pollution is more independent than might be assumed. The CCA analysis suggests that the strongest environmental gradient (CCA1) associated with lichen community composition includes regional pollution load and climatic variables; the second gradient (CCA2) is associated with local pollution load and human population density factors. These results increase the knowledge of lichen biodiversity for the Niagara Escarpment and urban and rural fragmented ecosystems as well as along gradients of human population density and air pollution; they suggest a differential influence of regional and local pollution loads and population density factors. This study provides baseline knowledge for further research and conservation initiatives along the Niagara Escarpment World Biosphere Reserve.

Local and regional habitat conditions associated with agricultural activity can fundamentally alter aquatic ecosystems. Increased nutrient inputs, channelization and reduced riparian habitat both upstream and locally contribute to the degradation of stream ecosystems and their function. Here, we examine stream food webs in watersheds that feed into Lake Erie to determine the effects of agricultural land cover on major food web energy pathways and trophic structure. Given that higher agricultural intensity can increase nutrient runoff and reduce the riparian zone and litter in-fall into streams, we predicted that generalist fish would derive less energy from the terrestrial pathway and become more omnivorous. Consistent with these predictions, we show that both mean terrestrial energy use and trophic position of the resident top consumer, creek chub ( Semotilus atromaculatus ), decrease with local agricultural intensity but not with watershed-level agriculture intensity. These findings suggest that local riparian buffers can maintain trophic structure even in the face of high whole-watershed agricultural intensity.

Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Despite species specific empirical examples of multi-tracer approaches to provenance, the precise benefit and efficacy of multi-tracers remains poorly understood. Here we show why, and when, data fusion of bio-tracers is an extremely powerful technique for geographical provenance discrimination. Specifically, we show using extensive simulations how, and under what conditions, geographical relationships between bio-tracers (e.g., spatial covariance) can act like a spatial fingerprint, in many naturally occurring applications likely allowing rapid identification with limited data. To highlight the theory, we outline several statistic methodologies, including artificial intelligence, and apply these methodologies as a proof of concept to a limited data set of 90 individuals of highly mobile Sockeye salmon that originate from 3 different areas. Using 17 measured bio-tracers, we demonstrate that increasing combined bio-tracers results in stronger discriminatory power. We argue such applications likely even work for such highly mobile and critical fisheries as tuna.