Strong inference research based on statistical measures has been and is Survivingenious' guiding principle during ECCwBD development. As the capability to quickly execute functions across large geospatial data improves, so do processing and display rates, helping us to see broad phenological changes within recent timeframes. Geographic information system graphical user interfaces running on local platforms are now linked with Cloud processing and have adequate functionality for branched algorithmic capabilities. Enhanced processing while running large geospatial data analyses is what makes ECCwBD possible. Preprocessing decentralized Big Data and remote geospatial data access during runtime is now here. Google Earth Engine offers the capability to code functions and access geospatial data for on-the-fly algorithmic execution. 

A great stride Google made with the advent of Google Server storage and the launch of Earth Engine in 2010 (Gorelick et al. 2017). Repositories of geospatial data offer the potential to accomplish ecologically informed geospatial predictive modeling. Ecological statistics in R and data access for local processing and analyses is made possible here thanks to R Shiny Server. ECCwBD is a JavaScript, R, user-friendly application capable of cloud-based processing and spatio-temporal analyses of large geospatial datasets within user defined areas of interest.

ECCwBD's intellectual merit draws inference from the scientifically vetted MOD17 algorithm primary productivity estimates. MOD17 incorporates BIOME-BGC ecosystem process model estimates with calibrated radiometric MODIS product suite data (albedo, leaf area index, fraction of absorbed photosynthetic radiation, temperature, and moisture), classified land cover types, evaporative transpiration rates, radiation conversion efficiencies, and estimated carbon fixation rates to calculate 500m gross primary productivity, net photosynthesis, and net primary productivity estimates worldwide (LP DAAC, ORNL_DAAC, Running et al. 2004). Survivingenious LLC aims to explore streamlining ECCwBD outputs into carrying capacity estimates for ungulate rangelands worldwide. All rangeland managers are welcome to participate and any published ECCwBD results are available worldwide to anyone with ECCwBD access.

Survivingenious believes, if we observe phenomena through the lenses of imagination and passion our images can become real. In summary, the ECCwBD geospatial analyses, summarization, and predictive modeling tool was created to benefit bison's (Bison bison) unique mutualistic relationships with flora and fauna across North America. As intrinsic biodiversity and ecosystem functions in North America were historically well adapted to bison migrations, bison numbers were kept in check by indigenous hunters who ingeniously sustained an expansive population of Native Americans. Now Americans have the potential to once again monitor and utilize large native herbivores through measurable responses across North American grasslands and beyond. Moreover, other large ungulates and their potential to be properly managed may also promote mutualistic relationships within interconnected nutrient cycling habitats. Survivingenious suggests adaptation as terrestrial and aquatic trophic cascades are further exasperated by increasingly warmer air and sea temperatures via disruption of deep sea currents and affected meteorological feedback loops. 

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May we now observe natural phenomena as it unfolds and use statistical measures to inform us of not the cause but only what is not. ECCwBD was inspired by and is dedicated to God. In effect, the ECCwBD application is free to use for everybody.