The New South Wales' (NSW) Government of Australia through the Office of Environment and Heritage (OEH) has recognised the potential of citizen science for improving environmental outcomes for NSW. Citizen science can achieve two primary objectives for government: (1) expanding the potential for information collection, analysis, and curation; and (2) engaging and motivating the community who become involved. It also has the potential to increase inter-agency collaboration to achieve state and national large-scale policy objectives. In 2016 OEH made a firm commitment to citizen science by creating a position focused on citizen science and releasing a three-year citizen science strategy. The vision of this strategy is to "drive a new era of public participation in science by developing collaborative projects that support decision-making and are engaging for the public." The emphasis on a real contribution by citizen scientists to decision-making is a critical and distinguishing part of this vision. To support this, OEH has a position statement defining the standards adhered to by OEH citizen science projects. Here we detail how the staff involved achieved agency support and endorsement for its citizen science program. We describe pilot studies implemented to demonstrate how citizen science can augment environmental monitoring and enhance the way OEH interacts with its citizen science community. We outline organisational challenges (e.g., data management, agility, and branding) that we encountered when establishing a citizen science program as well as the steps undertaken to address these challenges. We close by discussing some of the ways government can use citizen science as part of Australian policy delivery, and articulate ways in which the government can provide ongoing support to citizen science.
1. The Strategic Plan for Biodiversity (2011–2020), adopted at the 10th meeting of the Conference of the Parties to the Convention on Biological Diversity, sets 20 Aichi Biodiversity Targets to be met by 2020 to address biodiversity loss and ensure its sustainable and equitable use. Aichi Biodiversity Target 11 describes what an improved conservation network would look like for marine, terrestrial and inland water areas, including freshwater ecosystems. 2. To date, there is no comprehensive assessment of what needs to be achieved to meet Target 11 for freshwater biodiversity. Reports on implementation often fail to consider explicitly freshwater ecosystem processes and habitats, the pressures upon them, and therefore the full range of requirements and actions needed to sustain them. 3. Here the current progress and key gaps for meeting Aichi Target 11 are assessed by exploring the implications of each of its clauses for freshwater biodiversity. 4. Concerted action on Aichi Biodiversity Target 11 for freshwater biodiversity by 2020 is required in a number of areas: a robust baseline is needed for each of the clauses described here at national and global scales; designation of new protected areas or expansion of existing protected areas to cover known areas of importance for biodiversity and ecosystem services, and a representative sample of biodiversity; use of Other Effective Area-Based Conservation Measures (OECMs) in places where designating a protected area is not appropriate; and promoting and implementing better management strategies for fresh water in protected areas that consider its inherent connectivity, contextual vulnerability, and required human and technical capacity. 5. Considering the specific requirements of freshwater systems through Aichi Biodiversity Target 11 has longterm value to the Sustainable Development Goals discussions and global conservation policy agenda into the coming decades. ; VH was funded by a Ramon y Cajal contract (RYC-2013-13979) funded by the Spanish government. IH is grateful to Conservation International for funding his contributions to the 2014 World Parks Congress; the Department of Ichthyology, American Museum of Natural History, New York for granting Research Associate status; and Columbia University, New York for granting Adjunct Research Scientist Status, and allowing access to their library facilities. SL was funded by an ARC DECRA grant DE130100565.
Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals. ; This paper emerged from the first two workshops of the Horizon 2020 project GLOBIS‐B (GLOBal Infrastructures for Supporting Biodiversity research; http://www.globis‐b.eu/). Financial support came from the European Commission (grant 654003). C. A. additionally received funding from the LifeWatchGreece infrastructure (MIS 384676), funded by the Greek Government under the General Secretariat of Research and Technology (GSRT), ESFRI Projects and National Strategic Reference Framework (NSRF). M. O. was supported by the Swedish LifeWatch project funded by the Swedish Research Council (Grant no. 829‐2009‐6278), and J.E. by the Australian Research Council (grant FT0991640).
Although satellite‐based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS‐EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS‐EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS‐EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long‐term coordinated actions. ; DSS, RS, DR and JP were financed by the EU BON project that is a Seventh Framework Programme funded by the European Union under Contract No. 308454. ; Peer reviewed
