At the core of the campaign is better measurement of biodiversity patterns (by integrating GBIF and other data) and better support for the decision-making and planning needed to reduce biodiversity loss (by using systematic conservation planning).

While many have regarded the 2010 target as difficult to achieve, a reduced rate of biodiversity loss by 2010 is possible, based on the core idea of systematic conservation planning (SCP). Simply put, land-use planning and other decision making that more efficiently balances conservation with other needs of society implies reduced biodiversity losses, compared to business-as-usual. Through that important core idea of finding a balance, the GBIF2010 Campaign provides links to the Millennium Development Goal (MDG) of sustainability at global and regional scales.

The SCP approach depends on GBIF primary data as the basis for good measures of overall (wholesale) biodiversity. These data are integrated with environmental data to extend the predictive power of the biodiversity models. These models then must be integrated with socio-economic, threats, and land use data for SCP decisions and indicators of achievement against the 2010 target. In this way, the Campaign hopes to promote, demonstrate, and enable application of GBIF primary biodiversity data to not only measure progress towards, but actually achieve the 2010 biodiversity target.

The Campaign continues to develop case studies and communication of methods. For background to one developing case study, see link below (and see also link within that document to a figure showing 2010 scenarios).
Faith, DP, Williams, KJ (2006) Research needs and challenges for the "systematic conservation planning" approach to the 2010 biodiversity target. In: Actions for the 2010 biodiversity target in Europe - how does research contribute to halting biodiversity loss? Report of an e-conference. (eds. Young, J., Ahlbeg, M., Niemelä, N., Parr, T., Pauleit, S. and Watt, A.D.), pp. 27-29. Read full report

The Campaign is interested in proposals for new case studies based on new and existing GBIF data.