Measures of nutrient processes as indicators of stream ecosystem health

To better understand how freshwater ecosystems respond to changes in catchment land-use, it is important to develop measures of ecological health that include aspects of both ecosystem structure and function. This study investigated measures of nutrient processes as potential indicators of stream ecosystem health across a land-use gradient from relatively undisturbed to highly modified. A total of seven indicators (potential denitrification; an index of denitrification potential relative to sediment organic matter; benthic algal growth on artificial substrates amended with (a) N only, (b) P only, and (c) N and P; and delta N-15 of aquatic plants and benthic sediment) were measured at 53 streams in southeast Queensland, Australia. The indicators were evaluated by their response to a defined gradient of agricultural land-use disturbance as well as practical aspects of using the indicators as part of a monitoring program. Regression models based on descriptors of the disturbance gradient explained a large proportion of the variation in six of the seven indicators. Denitrification index, algal growth in N amended substrate, and delta N-15 of aquatic plants demonstrated the best regression. However, the delta N-15 value of benthic sediment was found to be the best indicator overall for incorporation into a monitoring program, as samples were relatively easy to collect and process, and were successfully collected at more than 90% of the study sites

The use of phospholipid fatty acid analysis to measure impact of acid rock drainage on microbial communities in sediments

The impact of acid rock drainage (ARD) and eutrophication on microbial communities in stream sediments above and below an abandoned mine site in the Adelaide Hills, South Australia, was quantified by PLFA analysis. Multivariate analysis of water quality parameters, including anions, soluble heavy metals, pH, and conductivity, as well as total extractable metal concentrations in sediments, produced clustering of sample sites into three distinct groups. These groups corresponded with levels of nutrient enrichment and/or concentration of pollutants associated with ARD. Total PLFA concentration, which is indicative of microbial biomass, was reduced by >70% at sites along the stream between the mine site and as far as 18 km downstream. Further downstream, however, recovery of the microbial abundance was apparent, possibly reflecting dilution effect by downstream tributaries. Total PLFA was >40% higher at, and immediately below, the mine site (0-0.1 km), compared with sites further downstream (2.5-18 km), even after accounting for differences in specific surface area of different sediment samples. The increased microbial population in the proximity of the mine source may be associated with the presence of a thriving iron-oxidizing bacteria community as a consequence of optimal conditions for these organisms while the lower microbial population further downstream corresponded with greater sediments' metal concentrations. PCA of relative abundance revealed a number of PLFAs which were most influential in discriminating between ARD-polluted sites and the rest of the sites. These PLFA included the hydroxy fatty acids: 2OH12:0, 3OH12:0, 2OH16:0; the fungal marker: 18:2ω6; the sulfate-reducing bacteria marker 10Me16:1ω7; and the saturated fatty acids 12:0, 16:0, 18:0. Partial constrained ordination revealed that the environmental parameters with the greatest bearing on the PLFA profiles included pH, soluble aluminum, total extractable iron, and zinc. The study demonstrated the successful application of PLFA analysis to rapidly assess the toxicity of ARD-affected waters and sediments and to differentiate this response from the effects of other pollutants, such as increased nutrients and salinity

An assessment framework for measuring agroecosystem health

<b>Highlights</b>\ud \ud - Pressure is increasing for agricultural industries to report on sustainability.\ud \ud - Industries currently report on agroecosystem health (AEH) using disparate methods.\ud \ud - We present a formal AEH assessment framework that is flexible and adaptive.\ud \ud - The framework includes diagnostic indicators explicitly linked to management.\ud \ud - It can be tailored to local issues and used to compare across regions or industries.\ud \ud <b>Abstract</b>\ud \ud There are inherent social, environmental, and economic trade-offs in agricultural systems, which by definition have been altered from their natural state by humans for food and fibre production. Consumers are increasingly concerned about the environmental and social impacts of agriculture, and with the increasing influence of social media, agribusinesses and industries can be held accountable for their actions in the public domain. Thus, environmental sustainability reporting is increasingly being viewed as a cost of doing business in agriculture. There are a number of approaches used to measure agroecosystem health (AEH) around the world, but they are generally designed to make comparisons at coarse spatial scales (i.e. nations) or report on specific management actions implemented at the local scale (i.e. farm, catchment, or sub-region). Here we present a simple, yet scientifically robust assessment framework that can be used to benchmark and monitor the specific impacts of agricultural management practices on the environment. The general principles are drawn from environmental monitoring and experiences gained in environmental assessments that are not necessarily agriculturally focussed. However, many commonly used environmental indicators are not suitable for \{AEH\} assessment because they do not explicitly link environmental outcomes to management actions; or they fail to separate specific agricultural impacts from broader cumulative impacts resulting from other industries or land uses. We recommend using a combination of diagnostic, outcome-based indicators, in addition to practice- and product-based measures to communicate efforts to improve agroecosystem health outcomes. The framework presented here enables assessments at local scales, but can be aggregated or disaggregated to report at finer or coarser scales. This flexibility ensures that the assessment is relevant to the proponent and stakeholders, while also providing a way to make comparisons between producers, industries, or regions as part of an adaptive monitoring and assessment framework. This also opens the door for industry-based \{AEH\} monitoring program to provide, or make use of information from government-funded environmental monitoring programs, with benefits to both

Appendix A. Bayesian model-averaging approach and stochastic model search.

Bayesian model-averaging approach and stochastic model search

Appendix B. Seasonal score model-averaged coefficients, standard errors, and inclusion probabilities.

Seasonal score model-averaged coefficients, standard errors, and inclusion probabilities

Foundations for the future: a long-term plan for Australian ecosystem science

Australia\u27s ecosystems are the basis of our current and future prosperity, and our national well-being. A strong and sustainable Australian ecosystem science enterprise is vital for understanding and securing these ecosystems in the face of current and future challenges. This Plan defines the vision and key directions for a national ecosystem science capability that will enable Australia to understand and effectively manage its ecosystems for decades to come. The Plan\u27s underlying theme is that excellent science supports a range of activities, including public engagement, that enable us to understand and maintain healthy ecosystems. Those healthy ecosystems are the cornerstone of our social and economic well-being. The vision guiding the development of this Plan is that in 20 years\u27 time the status of Australian ecosystems and how they change will be widely reported and understood, and the prosperity and well-being they provide will be secure. To enable this, Australia\u27s national ecosystem science capability will be coordinated, collaborative and connected. The Plan is based on an extensive set of collaboratively generated proposals from national town hall meetings that also form the basis for its implementation. Some directions within the Plan are for the Australian ecosystem science community itself to implement, others will involve the users of ecosystem science and the groups that fund ecosystem science. We identify six equal priority areas for action to achieve our vision: (i) delivering maximum impact for Australia: enhancing relationships between scientists and end-users; (ii) supporting long-term research; (iii) enabling ecosystem surveillance; (iv) making the most of data resources; (v) inspiring a generation: empowering the public with knowledge and opportunities; (vi) facilitating coordination, collaboration and leadership. This shared vision will enable us to consolidate our current successes, overcome remaining barriers and establish the foundations to ensure Australian ecosystem science delivers for the future needs of Australia