Network embedding for understanding the National Park System through the lenses of news media, scientific communication and biogeography

The United States national parks encompass a variety of biophysical and historical resources important for national cultural heritage. Yet how these resources are socially constructed often depends upon the beholder. Parks tend to be conceptualized according to their (fixed) geographic context, so our understanding of this system of systems is dominated by this geographic lens. To expose the systemic structure that exists beyond their geographic embedding, we analyze three representations of the national park system using park-park similarity networks according to their co-occurrence in: (a) ~423,000 news media articles; (b) ~11,000 research publications; and (c) ~60,000 species inhabiting parks. We quantify structural variation between network representations by leveraging similarity measures at different scales: park-level (park-park correlations) and system-level (network communities’ consistency). Because parks are governed and experienced at multiple scales, cross-network comparison informs how management should account for the varying objectives and constraints that dominate at each scale. Our results identify an interesting paradox: whereas park-level correlations depend strongly on the representative lens, the network communities are remarkably robust and consistent with the underlying geographic embedding. Our data-driven methodology is generalizable to other geographically embedded socio-environmental systems and supports the holistic analysis of systems-level structure that may elude other approaches.

Methods
To expose the systemic structure that exists beyond their geographic embedding, we analyze three representations of the national park system using park-park similarity networks according to their co-occurrence in: (a) ~423,000 news media articles; (b) ~11,000 research publications; and (c) ~60,000 species inhabiting parks. We quantify structural variation between network representations by leveraging similarity measures at different scales: park-level (park-park correlations) and system-level (network communities’ consistency).