Optimizing Network Science for Ecological Understanding.

Abstract:

Networks of observation and research sites such as those established by the National Ecological Observatory Network (NEON) and the Long-Term Ecological Research Network (LTER), coupled with the resources of the National Science Foundation Macrosystems Biology program create powerful opportunities for research at regional to continental scales that are otherwise not possible.

We illustrate how the adoption of existing network protocols (i.e. PhenoCam Network protocols) by another network (i.e. NEON) can work to address spatial gaps and expand network coverage to improve our understanding of phenology over coming decades. Additionally, we highlight how network resources are facilitating a multi-institutional, collaborative investigation to understand the distribution of soil organic matter (SOM) stocks, their stability and vulnerability at 40 NEON sites across eleven of the twelve soil orders. Soil cores for this study were collected as an oppurtunistic byproduct of network construction. Cores will be separated by genetic horizon, characterized according to standard laboratory physical and chemical analyses, and exposed to powerful quantitative assays including X-ray diffraction and radiocarbon analysis. The experimental design includes density fractionation to elucidate the dynamics of discrete SOM pools, as well as a long-term incubation experiments to understand SOM vulnerability to changes in soil moisture and temperature regimes.

While network science has already greatly advanced scientific understanding, its full power may be best realized by moving toward standardizing protocols and expanding these standardized measurements over space and time. It is also critical that networks maintain the flexibility to incorporate studies by individual investigators at some or all sites, adopt new methodologies, and evolve quickly in light of new community priorities. Funding opportunities or network resources (e.g. technician time for sample collection) directed at facilitating PI driven macrosystems scale research are also necessary to maximize results.