A semantically integrated, user-friendly data model for species observation data

Abstract

Recent decades have seen an increasing importance of large-scale ecological research, driven by increased awareness of the global influence of human activities on the biosphere. Such research requires species observation data covering many years, large areas and a broad range of taxonomic groups. As such data sets often cover small areas, and have been collected using varying methods, they can only be combined in a single analysis if they are made available at the same location and translated into a single format. Over the past decade, catalysed by the growth of the Internet, various technologies for data dissemination and data integration have been developed and applied in projects such as the Global Biodiversity Information Facility, the Knowledge Network for Biocomplexity, BioCASE and the British National Biodiversity Network (NBN). In the Netherlands, data are now made available from the National Database of Flora and Fauna (NDFF), which currently contains approximately 40 million observation records covering a broad variety of species. The NDFF uses a standardised, semantically integrated data model to combine effectively species observation data of various kinds. In this paper, we evaluate this approach and the NDFF data model, by comparison with Darwin Core, Access to Biological Collections Data (ABCD) and the Recorder 2000 model used by the NBN. We conclude that the high degree of standardisation in the NDFF data model has led to somewhat increased cost in data conversion, but also to improved semantic integration and ease-ofuse of species observation data. Together with the relative simplicity, completeness and flexibility of the model, this enables effective reuse of species observations in a user-friendly manner.

Biodiversity recovery following delta-wide measures for flood risk reduction

“…To understand the effects of landscape changes on functional biodiversity, we classified the species according to their dispersal rate and specificity of habitat requirements, leading to four functional groups per taxonomic group: “slow-spreading generalist,” “fast-spreading generalist,” “slow-spreading specialist,” and “fast-spreading specialist.” We ran BIOSAFE for all 179 freshwater floodplain sections in the Rhine River distributaries ( Fig. 1D ) with ecotope maps of 1997, 2005, 2008, and 2012, as well as species presence data from the NDFF ( 24 ). We distinguish “best-case” results that are based on all NDFF observations and “worst-case” results that are based on subsampled observations to rigorously compensate for the possible increase in sampling effort.…”

“…We acquired field observations of species presence to compute actual biodiversity parameters from the Dutch NDFF, which serves as the national data warehouse for exchanging species observation data ( 24 ). The database currently contains more than 90 million observations, bringing together observations from volunteers and professionals ( 42 ) based on 40 different field protocols (table S2).…”

“…Second, it constructs the species presence ( SP ) vector for each floodplain section based on the preprocessed NDFF species monitoring data, which is required for the actual biodiversity scores. Preprocessing of these data is necessary because monitoring data come in many file formats and database structures ( 24 ).…”

Towards global interoperability for supporting biodiversity research on essential biodiversity variables (EBVs)

“…This includes free accessibility of data and analysis tools (open access), standardisation and automation in data management and sharing and automated userfriendly processing pipelines (e.g. virtual laboratories) (Hardisty, Roberts, and The Biodiversity Informatics Community 2013; Hobern et al 2013;Veen et al 2012;Vicario, Hardisty, and Haitas 2011). Hence, for the worldwide uniform implementation of EBVs, biodiversity scientists and research infrastructure operators have to cooperate globally to facilitate the interoperability of data and associated processing tools.…”

“…The figure was created with Wordle (http://www.wordle.net/). enable the effective reuse of ecological data in a userfriendly manner (Veen et al 2012). Prerequisites are standardisation of data, metadata and data sharing (e.g.…”