A CyberCommons for Ecological Forecasting
Four universities in two EPSCoR states, Kansas and Oklahoma, are collaborating to create a cyberCommons, a powerful, integrated cyber environment for knowledge discovery and education across complex environmental phenomena. Specifically, the cyberCommons will integrate two frameworks—the science framework of data, models, analytics and narratives, and the cyberinfrastructure framework of hardware, software, collaboration environment and integration environment. Weaving these frameworks harnesses the enormous revolution in CI technologies for ecological forecasting across the Central Plains.
In so doing, the cyberCommons will make complex, cross-domain research on ecological systems collaborative for investigators, tractable for science, and beneficial for society.
Understanding ecological systems and forecasting their responses to global change is one of the grand challenges of the 21st century, as established by the National Research Council (NRC) and numerous other national and international bodies. Addressing this challenge is critical for grasslands, an ecosystem that is fundamental to the life and economy of the Central Plains, and provides vital goods and services to human societies worldwide—supplying clean water, recycling essential nutrients, sustaining biodiversity, and buffering against invasive species and emerging diseases. The Central Plains grasslands are second only to the Arctic tundra in sequestering carbon below ground.
At the same time, the impact of human activities, particularly food crop production, on Central Plains grasslands has altered land-use, land-cover and ecosystem structure and function on an unprecedented scale. This impact is being exacerbated by the demand for biofuels. Equally severe have been the consequences for biodiversity, a critical component of ecosystem function—species extinctions and extirpations, expansion of invasive species, and the spread of emerging zoonotic diseases.
Two scientific questions underlie the understanding and forecasting of ecological systems in the Central Plains. Without a unifying and fertile CI environment, their complexity will continue to elude comprehensive exploration and knowledge discovery.
1. What are the impacts of changes in land-use/land-cover and climate, both natural and
anthropogenic, on biogeochemical cycles and ecosystem function? In turn, what are the feedbacks among these drivers and consequences?
2. What are the impacts of changes in land-use/land-cover and climate, both natural and
anthropogenic, on biodiversity—its composition, patterns and dynamics? In turn, how do these changes in biodiversity affect the spread of plant and animal diseases and invasive species, and how do these phenomena influence ecosystem structure, function and services?
Essentially, these questions involve deciphering complex, non-linear, reciprocal impacts among primary drivers and consequences. Changes in climate and land-use/land-cover are the drivers; changes in biogeochemical cycles and biodiversity are the consequences, with feedbacks to the drivers. For example, abiotic changes in temperature, precipitation, N deposition and CO2 concentrations drive changes in biogeochemical cycles, grassland CO2 flux, the composition of plant and microbial communities and, ultimately, ecosystem structure and function. Likewise, widespread changes in landuse and land-cover on the Central Plains, such as conversion of native grasslands to cropland, and invasion of grasslands by woody species, affect ecosystem-level C and N storage, biogeochemical processes and biodiversity dynamics, including the invasion of non-native species and zoonotic diseases.
As articulated by the NRC, the epidemiology of zoonotic diseases is an ecologically mediated process, involving their pathogen-vector-host relationships, spatial and temporal dynamics, and consequent introduction, geographic spread and persistence. Fortunately for ecoforecasting, ecology and biodiversity science are data rich, with massive streams and stores of data from field sensor networks and experiments, remote sensing satellites, and the digital capture of 300-year legacy biotic collections of animals and plants in museums and herbaria. Unfortunately, many of these data lack the resolution, common spatial and temporal values, and the common ontologies for modeling and forecasting ecological processes across spatial scales.