Biogeochemists responded to the challenges of global change and the NEON opportunity with recommendations on variables and processes that need monitoring, experimentation, and instrumentation. Research plans for community ecology, including biodiversity, invasive species, disease, and habitat fragmentation are less clear. Science for each of these areas should exploit NEON to address fundamental problems concerning the maintenance of biodiversity and it's impacts. As the scientific stewards of biodiversity it is incumbent on community ecologists to sharpen the questions that would be answered by continent-wide research and the key variables and processes that need concomitant prioritization and quantification.
Thus far, recommendations on data to collect as part of a national network emphasizes the traditional paradigm by monitoring population abundances and densities as predictors, and population growth rates as response variables. Such data could be used in time series models of abundance as the basis for estimating parameters related to processes and for predicting trends but this approach may yield limited insight. Abundances change for many reasons, controls will be partially known, and time series analyses can be unenlightening.
Results/Conclusions

We suggest that demographic rates constitute fundamental transfers of matter and energy that can focus national-scale biodiversity science. Be it the variation within and among species, their interactions, and the habitats that support them, demographic rates are critical for hypothesis testing. Human epidemiology, public health, and urban planning testify to the importance of demographic data for our own species. Demography is already a prominent element of population ecology and conservation. The challenge now is to prioritize data collection from a limited number of species across a broad, but sparse, spatial array to understand and predict controls on biodiversity.
We provide some recommendations for data and how to use them. Demographic data need to be i) individual based, ii) longitudinal, iii) multicohort, and iv) they must include spatio-temporal covariate information. They would additionally benefit from controlled interventions that increase the range of covariate values and help break up correlations. Models must allow for integration of data and allow for inference on hidden states at multiple levels. Collectively, we draw inference on both 'the forest and the trees', i.e., the factors that affect individuals and their interactions within communities and a fluctuating physical environment. We provide examples of the detailed insights that could not be achieved with abundance monitoring and models based thereon.