Citizen science continues to make a substantial contribution to a wide variety of scientific disciplines by allowing the public to be involved in activities like idea generation, study design, and data collection and analysis. Although the pace of citizen science has exploded in recent decades, there remains untapped potential for scientific output through investment in research infrastructure (RI) that more specifically supports citizen science activities. Here, we provide a case study of how the biodiversity RI, the Atlas of Living Australia (ALA) has supported the growth of citizen science over the past decade by improving access to and utility of citizen science data and products, resulting in around 50% of the 115 million records in ALA coming from citizen scientists. We show that around one quarter of data collection projects provide around half of all species observation records in the ALA, supplementing specimen-based data to provide a more comprehensive picture of species distributions in Australia. We then discuss how RI, like the ALA, supports common citizen science data challenges by implementing tools to standardise complex data, to safely store sensitive data, and to improve participation and discoverability of citizen science data. Our findings demonstrate the importance of investment in open access research infrastructure to support and augment the scientific value of the citizen science movement globally.

The Atlas of Living Australia (ALA) is Australia’s national biodiversity database, delivering data and related services to more than 80,000 Australian and international users annually. Established under the Australian Government’s National Collaborative Research Infrastructure Strategy to provide trusted biodiversity data to support the research sector, its utility now extends to government, higher education, non-government organisations and community groups. These partners provide data to the ALA and leverage its data and related services. The ALA has also played an important leadership role internationally in the biodiversity informatics and infrastructure space, both through its partnership with the Global Biodiversity Information Facility and through support for the international Living Atlases programmes which has now delivered 24 instances of ALA software to deliver sovereign biodiversity data capability around the world. This paper begins with a historical overview of the genesis of the ALA from the collections, museums and herbaria community in Australia. It details the biodiversity and related data and services delivered to users with a primary focus on species occurrence records which represent the ALA's primary data type. Finally, the paper explores the ALA's future directions by referencing results from a recently completed national consultation process.

Australia invests significant resources in environmental data acquisition, management and publication. Although data are abundant, users are typically hampered by an inability to discover, access and re-use the data. The National Environmental Information Infrastructure (NEII) activity will improve the effectiveness and efficiency of discovering, accessing and re-using environmental data beyond its primary purpose. The NEII is envisioned as a federation of environmental data nodes adopting common standards to create a data infrastructure with an initial focus on nationally significant environmental data. Its primary focus is on the discovery and re-use of national environmental data that is already well-managed, but that currently has limited application beyond its original business purpose. In common with spatial data infrastructures, the NEII encompasses common data models, exchange formats and standard network protocols along with centralised catalogues of uniform metadata descriptions. It also includes standardised models for describing environmental measurements, monitoring sites and methods used to observe the environment to address the unique requirements of environmental information. These architectural elements are more fully described in the NEII Reference Architecture (Bureau of Meteorology, 2014b, www.neii.gov.au).The Bureau of Meteorology is leading the development of the NEII including provision of core coordination and integration infrastructure, as well as the governance and collaboration framework for its development and operation. However its ongoing success is dependent on establishing enduring partnerships with major national environmental information organisations. The NEII Programme is framed around five focus areas including (a) Communication, (b) Engagement, (c) Policies and frameworks, (d) Data management, and (e) ICT build. This paper provides a more detailed overview of one component of the Data Management area regarding the development of the NEII Conformance Framework.Conformance means following established guidelines, specifications and standards or working towards them. A conformance framework is a structured set of guidelines that detail the levels at which a participant provides services that comply with the NEII architecture. The NEII Conformance Framework has been developed to (a) enable partners to set priorities against specific expectations that will jointly lead to developing a sustainable federated environmental information system; (b) enable users of the NEII to rapidly assess the suitability of data for their business needs; and (c) provide a common measurement system to monitor progress in the development of the NEII. The framework adopts a capability maturity model approach to describe the steady improvement required for NEII nodes to progress from a basic data release (experimental data service with limited operational support) to an enduring federated system providing environmental information. The design of the model is informed by other maturity-based approaches for open data such as the Open Data Institute's certification approach (Open Data Institute 2015). The paper also presents a worked example of the conformance framework against the Bureau of Meteorology's Australian Hydrologic Geospatial Fabric NEII data services.The paper concludes with an overview of major learnings from the programme to-date. These relate primarily to reducing the cost of participation in NEII by data custodians given the challenge of delivering data using NEII standards; and developing approaches to better support users to use the portfolio of NEII data given some of the methods of data delivery may be new to non-technical users. The NEII programme has prioritised both these and developed work packages to achieve improvements.

Combining multiple proximal sensors within a wireless sensor network (WSN) enhances our capacity to monitor vegetation, compared to using a single sensor or non-networked setup. Data from sensors with different spatial and temporal characteristics can provide complementary information. For example, point-based sensors such as multispectral sensors which monitor at high temporal frequency but, at a single point, can be complemented by array-based sensors such as digital cameras which have greater spatial resolution but may only gather data at infrequent intervals. In this article we describe the successful deployment of a prototype system for using multiple proximal sensors (multispectral sensors and digital cameras) for monitoring pastures. We show that there are many technical issues involved in such a deployment, and we share insights relevant for other researchers who may consider using WSNs for an operational deployment for pasture monitoring under often difficult environmental conditions. Although the sensors and infrastructure are important, we found that other issues arise and that an end-to-end workflow is an essential part of effectively capturing, processing and managing the data from a WSN. Our deployment highlights the importance of testing and ongoing monitoring of the entire workflow to ensure the quality of data captured. We demonstrate that the combination of different sensors enhances our ability to identify sensor problems necessary to collect accurate data for pasture monitoring.

Effective biodiversity monitoring is critical to evaluate, learn from, and ultimately improve conservation practice. Well conceived, designed and implemented monitoring of biodiversity should: (i) deliver information on trends in key aspects of biodiversity (e.g. population changes); (ii) provide early warning of problems that might otherwise be difficult or expensive to reverse; (iii) generate quantifiable evidence of conservation successes (e.g. species recovery following management) and conservation failures; (iv) highlight ways to make management more effective; and (v) provide information on return on conservation investment. The importance of effective biodiversity monitoring is widely recognized (e.g. Australian Biodiversity Strategy). Yet, while everyone thinks biodiversity monitoring is a good idea, this has not translated into a culture of sound biodiversity monitoring, or widespread use of monitoring data. We identify four barriers to more effective biodiversity monitoring in Australia. These are: (i) many conservation programmes have poorly articulated or vague objectives against which it is difficult to measure progress contributing to design and implementation problems; (ii) the case for long-term and sustained biodiversity monitoring is often poorly developed and/or articulated; (iii) there is often a lack of appropriate institutional support, co-ordination, and targeted funding for biodiversity monitoring; and (iv) there is often a lack of appropriate standards to guide monitoring activities and make data available from these programmes. To deal with these issues, we suggest that policy makers, resource managers and scientists better and more explicitly articulate the objectives of biodiversity monitoring and better demonstrate the case for greater investments in biodiversitymonitoring. There is an urgent need for improved institutional support for biodiversity monitoring in Australia, for improved monitoring standards, and for improved archiving of, and access to, monitoring data. We suggest that more strategic financial, institutional and intellectual investments in monitoring will lead to more efficient use of the resources available for biodiversity conservation and ultimately better conservation outcomes.

This paper describes the development and testing of an automated method for detecting change in groundcover vegetation in response to kangaroo grazing using visible wavelength digital photography. The research is seen as a precursor to the future deployment of autonomous vegetation monitoring systems (environmental sensor networks). The study was conducted over six months with imagery captured every 90min and post-processed using supervised image processing techniques. Synchronous manual assessments of groundcover change were also conducted to evaluate the effectiveness of the automated procedures. Results show that for particular cover classes such as Live Vegetation and Bare Ground, there is excellent temporal concordance between automated and manual methods. However, litter classes were difficult to consistently differentiate. A limitation of the method is the inability to effectively deal with change in the vertical profile of groundcover. This indicates that the three dimensional structure related to species composition and plant traits play an important role in driving future experimental designs. The paper concludes by providing lessons for conducting future groundcover monitoring experiments.

Regional-scale ecological restoration priorities such as increasing the extent and quality of native vegetation are generally planned at catchment scales, while on-ground restoration actions are generally implemented at paddock or farm scales. This paper describes the use of spatial multi-criteria assessment methodologies to construct maps of regional conservation priorities and assesses how these maps map influence farm-scale actions in Western Victoria, Australia (e.g. farm-scale revegetation for salinity, wind erosion, stock shelter, etc). The study also incorporates agricultural production in the decision analysis through the use of historical yield mapping data obtained from harvest logs from precision agriculture equipment. Via a stakeholder workshop, farmer land use priorities were elicited with and without access to maps of regional conservation priorities. Results highlight that production imperatives drive farmer-led conservation actions and that regional conservation priorities have only limited impact on actions. The paper also identifies limitations of applying MCA methods across multiple decision-making scales such as the need to generalise priorities where domain knowledge is relatively high, and the challenges associated with MCA criteria definition.

Regional-scale native vegetation mapping in Australia has traditionally focussed on the detection of woody vegetation. However there is an increasing recognition that transformation of the native herbaceous ground layer to exotic species is an equally important element of vegetation loss. In grass-dominated vegetation, the ground layer contains most of the plant diversity and hence its condition is critical for maintaining the ecological function of grasslands and woodlands. However, decades of fertiliser use have transformed native pastures to exotic dominance and there is a need for regional-scale ground layer condition assessment. We used time-series Landsat 5 imagery, combined with rainfall and soil moisture data, and coupled with site-based soil phosphorus data to develop assessments of ground layer nutrient status from spectral indices. We found significant relationships between soil available phosphorus and NDVI seasonal change. A constraint in the exploration of this relationship is the need for active plant growth, and therefore sufficient rainfall for a signal to be detected. Similarly performing models were derived from two time periods and while they differed in their direction of response, both were able to predict sites with low P. Differences were due to the functional attributes of the dominant vegetation that were active at the time of image collection. Further refinement of the models should be achievable by designing plot sampling strategies more aligned to synoptic remote sensing needs, and incorporating plant composition data into the analysis.

The need for public investment to address loss of biodiversity in agricultural landscapes is well recognised, yet there is little analysis of the likely benefits of land-use change for regional biodiversity or the cost effectiveness of different investment options. We estimated benefits for biodiversity and cost effectiveness of different investment scenarios over 50 years for a farming area in south-eastern Australia. Declines in biodiversity were predicted under status quo land use. Implementing actions in the investment scenarios improved biodiversity status only slightly, compared with status quo land use. Future biodiversity status differed little between biodiversity-focused investment and salinity-focused investment. Biodiversity status equalled or exceeded current status only for investment scenarios with much more extensive revegetation than in catchment targets. Cost effectiveness of biodiversity improvement varied greatly between investment strategies. Biodiversity improvement was more cost effective when investment to meet catchment targets was focused on revegetation for salinity management rather than on high conservation value areas, because of lower opportunity costs for salinity management. With enhanced investment, the cost effectiveness of biodiversity improvement was greater when actions were in high conservation areas. Although improvements in biodiversity were small under the changed farming system scenarios, their cost effectiveness was higher than the other investment scenarios. Regional scale improvements in biodiversity in farming areas will require increased stewardship payments or other economic incentives for landholders.

Farmland biodiversity is greatly enhanced by the presence of trees. However, farmland trees are declining worldwide, including in North America, Central America, and parts of southern Europe. We show that tree decline and its likely consequences are particularly severe in Australia's temperate agricultural zone, which is a threatened ecoregion. Using field data on trees, remotely sensed imagery, and a demographic model for trees, we predict that by 2100, the number of trees on an average farm will contract to two-thirds of its present level. Statistical habitat models suggest that this tree decline will negatively affect many currently common animal species, with predicted declines in birds and bats of up to 50% by 2100. Declines were predicted for 24 of 32 bird species modeled and for all of six bat species modeled. Widespread declines in trees, birds, and bats may lead to a reduction in economically important ecosystem services such as shade provision for livestock and pest control. Moreover, many other species for which we have no empirical data also depend on trees, suggesting that fundamental changes in ecosystem functioning are likely. We conclude that Australia's temperate agricultural zone has crossed a threshold and no longer functions as a self-sustaining woodland ecosystem. A regime shift is occurring, with a woodland system deteriorating into a treeless pasture system. Management options exist to reverse tree decline, but new policy settings are required to encourage their widespread adoption.

Agriculture and livestock grazing threaten biodiversity around the world. In the grazing landscapes of eastern Australia, a common conservation strategy has been to exclude livestock from large patches of trees (typically > 5 ha). This has major local benefits, but is unlikely to stem regional biodiversity loss. Using a case study from the Upper Lachlan catchment in New South Wales, we show that (1) approximately 30% of tree cover occurs as very small patches or scattered trees; (2) large patches have disappeared from 90% of the landscape; and (3) large patches are 3.5 times more likely to be in unproductive upland areas than in lowland areas of high conservation concern. Given the limitations of focusing on large patches of trees to achieve regional conservation outcomes, the next generation of conservation initiatives should consider a new suite of additional measures that could deliver biodiversity benefits across broad areas of the region. Two key measures that must be considered are new incentives for farmers to alter livestock grazing practices and reduce fertilizer use.

Environmental sensor networks (ESNs) provide new opportunities for improving our understanding of the environment. In contrast to remote sensing technologies where measurements are made from large distances (e.g. satellite imagery, aerial photography, airborne radiometric surveys), ESNs focus on measurements that are made in close proximity to the target environmental phenomenon. Sensors can be used to collect a much larger number of measurements, which are quantitative and repeatable. They can also be deployed in locations that may otherwise be difficult to visit regularly. Sensors that are commonly used in the environmental sciences include ground-based multispectral vegetation sensors, soil moisture sensors, GPS tracking and bioacoustics for tracking movement in wild and domesticated animals. Sensors may also be coupled with wireless networks to more effectively capture, synthesise and transmit data to decision-makers. The climate and weather monitoring domains provide useful examples of how ESNs can provide real-time monitoring of environmental change (e.g. temperature, rainfall, sea-surface temperature) to many users. The objective of this review is to examine state-of-the-art use of ESNs for three environmental monitoring domains: (a) terrestrial vegetation, (b) animal movement and diversity, and (c) soil. Climate and aquatic monitoring sensor applications are so extensive that they are beyond the scope of this review. In each of the three application domains (vegetation, animals and soils) we review the technologies, the attributes that they sense and briefly examine the technical limitations. We conclude with a discussion of future directions.