Non-linear principal components analysis : an alternative method for finding patterns in environmental data

The main purpose of this article is to gain an insight into the relationships between variables describing the environmental conditions of the Far Northern section of the Great Barrier Reef, Australia. Several of the variables describing these conditions had different measurement levels and often they had non-linear relationships. Using non-linear principal component analysis, it was possible to acquire an insight into these relationships. Furthermore, three geographical areas with unique environmental characteristics could be identified.

Assessing community resilience to climate change

Settlements and communities in the Great Barrier Reef (GBR) are highly vulnerable to climate change and face an uncertain social, economic and environmental future. The concept of community resilience is gaining momentum as stakeholders and institutions seek to better understand the social, economic and governance factors which affect community capacity to adapt in the face of climate change. This paper defines a framework to benchmark community resilience and applies it to a case study in the Wet Tropics in tropical Queensland within the GBR catchment. It finds that rural, indigenous and some urban populations are highly vulnerable and sensitive to climate change, particularly in terms of economic vitality, community knowledge, aspirations and capacity for adaptation. Without early and substantive action, this could result in declining social and economic wellbeing and natural resource health. Capacity to manage the possible shocks associated with the impacts of climate change and extreme climatic events is emerging and needs to be carefully fostered and further developed to achieve broader community resilience outcomes. Better information about what actions, policies and arrangements build community resilience and mobilise adaptive capacity in the face of climate change is needed.

Uninhabited

"A young couple go to a remote and deserted coral island for a camping holiday, only to find that the island is inhabited by a ghost seeking retribution for a past outrage. - Written by Bill Bennett " "Synopsis: Based on actual events…. Harry and Beth want a different kind of holiday. So they charter a boat to drop them off on a remote coral island on the Great Barrier Reef. The island is idyllic – surrounded by a wide reef, covered in palms and full of birds and other wildlife, small and totally deserted. Or is it? The young lovers soon come to believe there is someone else on the island. Things go missing from their camp – and then they discover someone else’s footprints in the sand. What they didn’t realise was that the island has a ghost – a young girl who had died in shocking circumstances some eighty years earlier. The ghost at first plays mischievously with the young couple, but then turns malevolent. And their idyllic island holiday becomes a nightmare."

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making

Hormone and Metabolite Profiles in Nesting Green and Flatback Turtles: Turtle Species with Different Life Histories

Herbivorous turtle, Chelonia mydas, inhabiting the south China Sea and breeding in Peninsular Malaysia, and Natator depressus, a carnivorous turtle inhabiting the Great Barrier Reef and breeding at Curtis Island in Queensland, Australia, differ both in diet and life history. Analysis of plasma metabolites levels and six sex steroid hormones during the peak of their nesting season in both species showed hormonal and metabolite variations. When compared with results from other studies progesterone levels were the highest whereas dihydrotestosterone was the plasma steroid hormone present at the lowest concentration in both C. mydas and N. depressus plasma. Interestingly, oestrone was observed at relatively high concentrations in comparison to oestradiol levels recorded in previous studies suggesting that it plays a significant role in nesting turtles. Also, hormonal correlations between the studied species indicate unique physiological interactions during nesting. Pearson correlation analysis showed that in N. depressus the time of oviposition was associated with elevations in both plasma corticosterone and oestrone levels. Therefore, we conclude that corticosterone and oestrone may influence nesting behaviour and physiology in N. depressus. To summarise, these two nesting turtle species can be distinguished based on the hormonal profile of oestrone, progesterone, and testosterone using discriminant analysis.

The evolving landscape of natural resource planning and governance in Australia

Australia’s governance of land and natural resources involves multiple polycentric domains of decision-making from global through to local levels. Although certainly complex, these arrangements have not necessarily translated into better decision-making or better environmental outcomes as evidenced by the growing concerns over the health and future of the Great Barrier Reef, (GBR). However within this system, arrangements for natural resource management (NRM) and reef water quality, which both use Australia’s integrated regional NRM model, have showed signs of improving decision-making and environmental outcomes in the GBR. In this paper we describe the latest evolutions in the governance and planning for natural resource use and management in Australia. We begin by reviewing the experience with first generation NRM as published in major audits and evaluations. As our primary interest is the health and future of the GBR, we then consider the impact of changes of second generation planning and governance outcomes in Queensland. We find that first generation plans, although developed under a relatively cohesive governance context, faced substantial problems in target setting, implementation, monitoring and review. Despite this, they were able to progress improvements in water quality in the Great Barrier Reef Regions. Second generation plans, currently being developed, face an even greater risk of failure due to the lack of bilateralism and cross-sectoral cooperation across the NRM governance system. The findings highlight the critical need to re-build and enhance the regional NRM model for NRM planning to have a positive impact on environmental outcomes in the GBR.

Translating Management Storylines: developing a visual strategy to communicate science to stakeholder community, a case for Mackay, QLD

This project developed a visual strategy and graphic outcomes to communicate the results of a scientific collaborative project to the Mackay community. During 2013 and 2014 a team from CSIRO engaged with the community in Mackay to collaboratively develop a set of strategies to improve the management of the Great Barrier Reef. The result of this work was a 300+ page scientific report that needed to be translated and summarised to the general community. The aim of this project was to strategically synthesise information contained in the report and to design and produce an outcome to be distributed to the participant community. By working with the CISRO researchers, an action toolkit was developed, with twelve cards and a booklet. Each card represented the story behind a certain local management issue and the actions that the participants suggested should be taken in order to improve management of The Reef. During the design synthesis it was identified that for all management issues there was a reference to the need to develop some sort of "educational campaign" to the area. That was then translated as an underlying action to support all other actions proposed in the toolkit.

Robotic detection and tracking of Crown-Of-Thorns starfish

This paper presents a novel vision-based underwater robotic system for the identification and control of Crown-Of-Thorns starfish (COTS) in coral reef environments. COTS have been identified as one of the most significant threats to Australia's Great Barrier Reef. These starfish literally eat coral, impacting large areas of reef and the marine ecosystem that depends on it. Evidence has suggested that land-based nutrient runoff has accelerated recent outbreaks of COTS requiring extensive use of divers to manually inject biological agents into the starfish in an attempt to control population numbers. Facilitating this control program using robotics is the goal of our research. In this paper we introduce a vision-based COTS detection and tracking system based on a Random Forest Classifier (RFC) trained on images from underwater footage. To track COTS with a moving camera, we embed the RFC in a particle filter detector and tracker where the predicted class probability of the RFC is used as an observation probability to weight the particles, and we use a sparse optical flow estimation for the prediction step of the filter. The system is experimentally evaluated in a realistic laboratory setup using a robotic arm that moves a camera at different speeds and heights over a range of real-size images of COTS in a reef environment.

Statistical modelling and power analysis for detecting trends in total suspended sediment loads

The export of sediments from coastal catchments can have detrimental impacts on estuaries and near shore reef ecosystems such as the Great Barrier Reef. Catchment management approaches aimed at reducing sediment loads require monitoring to evaluate their effectiveness in reducing loads over time. However, load estimation is not a trivial task due to the complex behaviour of constituents in natural streams, the variability of water flows and often a limited amount of data. Regression is commonly used for load estimation and provides a fundamental tool for trend estimation by standardising the other time specific covariates such as flow. This study investigates whether load estimates and resultant power to detect trends can be enhanced by (i) modelling the error structure so that temporal correlation can be better quantified, (ii) making use of predictive variables, and (iii) by identifying an efficient and feasible sampling strategy that may be used to reduce sampling error. To achieve this, we propose a new regression model that includes an innovative compounding errors model structure and uses two additional predictive variables (average discounted flow and turbidity). By combining this modelling approach with a new, regularly optimised, sampling strategy, which adds uniformity to the event sampling strategy, the predictive power was increased to 90%. Using the enhanced regression model proposed here, it was possible to detect a trend of 20% over 20 years. This result is in stark contrast to previous conclusions presented in the literature. (C) 2014 Elsevier B.V. All rights reserved.

Sediment concentration prediction and statistical evaluation for annual load estimation

We consider the development of statistical models for prediction of constituent concentration of riverine pollutants, which is a key step in load estimation from frequent flow rate data and less frequently collected concentration data. We consider how to capture the impacts of past flow patterns via the average discounted flow (ADF) which discounts the past flux based on the time lapsed - more recent fluxes are given more weight. However, the effectiveness of ADF depends critically on the choice of the discount factor which reflects the unknown environmental cumulating process of the concentration compounds. We propose to choose the discount factor by maximizing the adjusted R-2 values or the Nash-Sutcliffe model efficiency coefficient. The R2 values are also adjusted to take account of the number of parameters in the model fit. The resulting optimal discount factor can be interpreted as a measure of constituent exhaustion rate during flood events. To evaluate the performance of the proposed regression estimators, we examine two different sampling scenarios by resampling fortnightly and opportunistically from two real daily datasets, which come from two United States Geological Survey (USGS) gaging stations located in Des Plaines River and Illinois River basin. The generalized rating-curve approach produces biased estimates of the total sediment loads by -30% to 83%, whereas the new approaches produce relatively much lower biases, ranging from -24% to 35%. This substantial improvement in the estimates of the total load is due to the fact that predictability of concentration is greatly improved by the additional predictors.

Load estimation with uncertainties from opportunistic sampling data: A semiparametric approach

We consider estimating the total load from frequent flow data but less frequent concentration data. There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates that minimizes the biases and makes use of informative predictive variables. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized rating-curve approach with additional predictors that capture unique features in the flow data, such as the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and the discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. Forming this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach for two rivers delivering to the Great Barrier Reef, Queensland, Australia. One is a data set from the Burdekin River, and consists of the total suspended sediment (TSS) and nitrogen oxide (NO(x)) and gauged flow for 1997. The other dataset is from the Tully River, for the period of July 2000 to June 2008. For NO(x) Burdekin, the new estimates are very similar to the ratio estimates even when there is no relationship between the concentration and the flow. However, for the Tully dataset, by incorporating the additional predictive variables namely the discounted flow and flow phases (rising or recessing), we substantially improved the model fit, and thus the certainty with which the load is estimated.

Spatial and temporal distribution of tropical phytoplankton species and biomass in the Gulf of Carpenteria, Australia

The biomass and species composition of tropical phytoplankton in Albatross Bay, Gulf of Carpentaria, northern Australia, were examined monthly for 6 yr (1986 to 1992). Chlorophyll a (chl a) concentrations were highest (2 to 5.7 mu g l(-1)) in the wet season at inshore sites, usually coinciding with low salinities (30 to 33 ppt) and high temperatures (29 to 32 degrees C). At the offshore sites chi a concentrations were lower (0.2 to 2 mu g l(-1)) and did not vary seasonally. Nitrate and phosphate concentrations were generally low (0 to 3.68 mu M and 0.09 to 3 mu M for nitrate and phosphate respectively), whereas silicate was present in concentrations in the range 0.19 to 13 mu M. The phytoplankton community was dominated by diatoms, particularly at the inshore sites, as determined by a combination of microscopic and high-performance liquid chromatography (HPLC) pigment analyses. At the offshore sites the proportion of green flagellates increased. The cyanobacterium genus Trichodesmium and the diatom genera Chaetoceros, Rhizosolenia, Bacteriastrum and Thalassionema dominated the phytoplankton caught in 37 mu m mesh nets; however, in contrast to many other coastal areas studied worldwide there was no distinct species succession of the diatoms and only Trichodesmium showed seasonal changes in abundance. This reflects a stable phytoplankton community in waters without pulses of physical and chemical disturbances. These results are discussed in the context of the commercial prawn fishery in the Gulf of Carpentaria and the possible effect of phytoplankton on prawn larval growth and survival.

A spatial assessment of the risk to a mobile marine mammal from bycatch

Several species of marine mammals are at risk of extinction from being captured as bycatch in commercial fisheries. Various approaches have been developed and implemented to address this bycatch problem, including devices and gear changes, time and area closures and fisheries moratoria. Most of these solutions are difficult to implement effectively, especially for artisanal fisheries in developing countries and remote regions. Re-zoning of the Great Barrier Reef World Heritage Area (GBRWHA) in 2004 closed 33% of the region to extractive activities, including commercial fishing. However, the impact of re-zoning and the associated industry restructuring on a threatened marine mammal, the dugong (Dugong dugon), is difficult to quantify. Accurate information on dugong bycatch in commercial nets is unavailable because of the large geographic extent of the GBRWHA, the remoteness of the region adjacent to the Cape York Peninsula where most dugongs occur and the artisanal nature of the fishery. In the face of this uncertainty, a spatial risk-assessment approach was used to evaluate the re-zoning and associated industry restructuring for their ability to reduce the risk of dugong bycatch from commercial fisheries netting. The new zoning arrangements appreciably reduced the risk of dugong bycatch by reducing the total area where commercial netting is permitted. Netting is currently not permitted in 67% of dugong habitats of high conservation value, a 56% improvement over the former arrangements. Re-zoning and industry restructuring also contributed to a 22% decline in the spatial extent of conducted netting. Spatial risk assessment approaches that evaluate the risk of mobile marine mammals from bycatch are applicable to other situations where there is limited information on the location and intensity of bycatch, including remote regions and developing countries where resources are limited.

Developing sustainable banana production systems: a case study from tropical Australia

The north Queensland banana industry is under pressure from government and community expectations to exhibit good environmental stewardship. The industry is situated on the high-rainfall north Queensland coast adjacent to 2 natural icons, the Great Barrier Reef to the east and World Heritage-listed rain forest areas to the west. The main environmental concern is agricultural industry pollutants harming the Great Barrier Reef. In addition to environmental issues the banana industry also suffers financial pressure from declining margins and production loss from tropical cyclones. As part of a broader government strategy to reduce land-based pollutants affecting the Great Barrier Reef, the formation of a pilot banana producers group to address these environmental and economic pressures was facilitated. Using an integrated farming systems approach, we worked collaboratively with these producers to conduct an environmental risk assessment of their businesses and then to develop best management practices (BMP) to address environmental concerns. We also sought input from technical experts to provide increased rigour for the environmental risk assessment and BMP development. The producers' commercial experience ensured new ideas for improved sustainable practices were constantly assessed through their profit-driven 'filter' thus ensuring economic sustainability was also considered. Relying heavily on the producers' knowledge and experience meant the agreed sustainable practices were practical, relevant and financially feasible for the average-sized banana business in the region. Expert input and review also ensured that practices were technically sound. The pilot group producers then implemented and adapted selected key practices on their farms. High priority practices addressed by the producers group included optimizing nitrogen fertilizer management to reduce runoff water nitrification, developing practical ground cover management to reduce soil erosion and improving integrated pest management systems to reduce pesticide use. To facilitate wider banana industry understanding and adoption of the BMP's developed by the pilot group, we conducted field days at the farms of the pilot group members. Information generated by the pilot group has had wider application to Australian horticulture and the process has been subsequently used with the north Queensland sugar industry. Our experiences have shown that integrated farming systems methodologies are useful in addressing complex issues like environmental and economic sustainability. We have also found that individual horticulture businesses need on-going technical support for change to more sustainable practices. One-off interventions have little impact, as farm improvement is usually an on-going incremental process. A key lesson from this project has been the need to develop practical, farm scale economic tools to clarify and demonstrate the financial impact of alternative management practices. Demonstrating continued profitability is critical to encourage widespread industry adoption of environmentally sustainable practices

A free-enzyme catalyst for the bioremediation of environmental atrazine contamination.

Herbicide contamination from agriculture is a major issue worldwide, and has been identified as a threat to freshwater and marine environments in the Great Barrier Reef World Heritage Area in Australia. The triazine herbicides are of particular concern because of potential adverse effects, both on photosynthetic organisms and upon vertebrate development. To date a number of bioremediation strategies have been proposed for triazine herbicides, but are unlikely to be implemented due to their reliance upon the release of genetically modified organisms. We propose an alternative strategy using a free-enzyme bioremediant, which is unconstrained by the issues surrounding the use of live organisms. Here we report an initial field trial with an enzyme-based product, demonstrating that the technology is technically capable of remediating water bodies contaminated with the most common triazine herbicide, atrazine.