Suchergebnisse

Changes in surface water availability for the environment (inflows minus diversions; highlighted in grey) in the Murray-Darling Basin (GL yr-1), calculated from inflow and diversion data in CSIRO (2008) and MDBA (2012). CSIRO (2008) Water Availability in the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. CSIRO, Canberra. Available at: http://www.csiro.au/science/MDBSY.html MDBA (2012) Hydrologic Modelling to Inform the Proposed Basin Plan: Methods and Results. MDBA Publication No. 17/12. Murray-Darling Basin Authority, Canberra.

An emerging planning framework for climate adaptation focuses on interactions among societal values, institutional rules and scientific and experiential knowledge about biophysical impacts of climate change and adaptation options. These interactions shape the decision context that can enable or constrain effective adaptation. To illustrate the operationalisation of this 'values-rules-knowledge' (VRK) framework we developed biophysical adaptation pathways for agricultural landscapes of south-eastern Australia, which are expected to become warmer and drier under climate change. We used the VRK framework to identify potential constraints to implementing the pathways. Drawing on expert knowledge, published literature, biodiversity modelling and stakeholder workshops we identified potential adaptation pathways for (1) the production matrix, (2) high conservation value remnant eucalypt woodlands, and (3) woodland trees. Adaptation options included shifts from mixed cropping-grazing to rangeland grazing or biomass enterprises; promoting re-assembly of native ecological communities; and maintaining ecosystem services and habitat that trees provide. Across all pathways, applying the VRK framework elucidated fifteen key implementation constraints, including limits to farm viability, decreasing effectiveness of environmental legislation and conflicting values about exotic plants. Most of the constraints involved interactions among VRK; 13 involved rules, eight involved values, and seven involved knowledge. Value constraints appeared most difficult to address, whereas those based on rules or knowledge were more tangible. The lower number of knowledge constraints may reflect the scale of our analysis (which focused on decision points in pre-defined pathways); new knowledge and participatory approaches would likely yield a richer set of scenarios. We conclude that the VRK framework helps connect the biophysical knowledge-based view of adaptation with a perspective on the need for changes in social systems, enabling targeting of constraints to adaptation. Our focus on pathways and decision points in different sectors of the multi-use landscape highlighted the importance of group and higher level planning and policy for balancing the collective outcomes of multiple decisions by many land managers. ; This research was funded by CSIRO Land and Water and contributes to the CSIRO Enabling Adaptation Pathways Project (EAP) and the Transformative Adaptation Research Alliance (TARA), an international network of researchers and practitioners dedicated to the development and implementation of novel approaches to transformative adaptation to global change.

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).