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Currently available global biodiversity functional data is grossly incomplete and non-representative taxonomically, geographically, environmentally, temporally, and functionally. Datasets on species functional characteristics (traits) continue to grow,  but the amount of digitally accessible information is meager, and even the most impressive efforts to assemble and aggregate datasets, such as the TRY plant function database has orders of magnitude data gaps when those data are considered not by taxon, but spatially. Simply put, for vast areas of the globe we have extremely limited data on trait diversity.  However, it is exactly these data on traits that form the central link between the evolutionary history of organisms, their assembly into communities, and the nature and dynamic functioning of ecosystems. As such, traits integrate the genetic architecture of life, past and present, with its biospheric consequences.  Developing a more global view on trait diversity requires not simply collating "speciesXtrait databases" but instead requires that individual specimens and their associated environmental context are measured, and that those measurements are available digitally. Such phenotypic variation among individuals of a species provides insight into biological mechanisms at different spatial and temporal scales.  We present knowledge models and tools that bridge the gap between assembling trait data at the specimen level and species level.  In particular, we show exemplar approaches to leveraging existing infrastructure for image annotation and trait ontologies in order to generate a more linked open data framework along with resources for assembling data as producers and consumers.   While the examples remain more theoretical than implemented, we show the power of datastores that allow proper provenance and aggregation and disaggregation of trait information usable broadly across the biodiversity and biogeography domain.  We also show how we plan to re-use tools that come from the biomedical domain.