The rebound effect on water extraction from subsidising irrigation infrastructure in Australia

Over the past decade, Australia has been buying water entitlements and subsidising irrigation infrastructure to reallocate water from consumptive to environmental purposes in the Murray-Darling Basin (MDB). There is considerable evidence that irrigation infrastructure subsidies are not cost-effective, as well as questions as to whether water extractions are increasing (rebounding) as a result. We used 2481 on-farm MDB irrigation surveys and identified a ‘rebound effect’ on water extractions, with irrigators who received an irrigation infrastructure subsidy significantly increasing (21-28%) their water extraction, relative to those who did not receive any grants. Although the precise hydrological impact of this rebound effect on catchment and Basin-wide extractions remains unknown, publicly available water data suggest that reductions in extractions from the MDB – supposedly commensurate with increases in environmental flows – may have been overestimated, particularly in the Northern MDB. This overestimation may in turn be linked to issues with water measurement and extractions at the catchment and Basin-scale, which occur due to: (1) water theft and poor enforcement; (2) inaccurate or absent water metering; (3) growth in unlicensed surface and groundwater extractions and on-farm storage capacity; (4) legal and practical uncertainties in compliance tools, processes and water accounting; and (5) complexity of floodplain, evaporation and groundwater interactions. To respond to these water governance challenges, MDB water and rural policy actions must: (1) improve measurement of diversions and develop transparent and robust water accounting, independently audited and accounting for uncertainty; (2) improve compliance, fines and regulation; (3) use multiple lines of evidence for water accounting and compliance; and (4) prioritise the cost and environmental effectiveness of water recovery

A combinatorial optimization approach for diverse motif finding applications

BACKGROUND: Discovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology. Most frequently, motif finding applications arise when identifying shared regulatory signals within DNA sequences or shared functional and structural elements within protein sequences. Due to the diversity of contexts in which motif finding is applied, several variations of the problem are commonly studied. RESULTS: We introduce a versatile combinatorial optimization framework for motif finding that couples graph pruning techniques with a novel integer linear programming formulation. Our approach is flexible and robust enough to model several variants of the motif finding problem, including those incorporating substitution matrices and phylogenetic distances. Additionally, we give an approach for determining statistical significance of uncovered motifs. In testing on numerous DNA and protein datasets, we demonstrate that our approach typically identifies statistically significant motifs corresponding to either known motifs or other motifs of high conservation. Moreover, in most cases, our approach finds provably optimal solutions to the underlying optimization problem. CONCLUSION: Our results demonstrate that a combined graph theoretic and mathematical programming approach can be the basis for effective and powerful techniques for diverse motif finding applications

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

Investigation of the causes of mass fish kills in the Menindee Region NSW over the summer of 2018–2019

On 15 December 2018 tens of thousands of dead fish were reported along a 30 km stretch of the Darling River near the town of Menindee in New South Wales. High numbers of dead fish were seen in the vicinity of the Old Menindee Weir and Menindee Pump Station. A second, larger fish kill event involving hundreds of thousands of fish was reported on 6 January 2019 on the same stretch of river. A third event followed on 28 January, killing millions of fish. Members of the panel witnessed the beginnings of a fourth event on 4 February 2019. Many different sectors of Australian society, and of the Menindee region itself, are distressed knowing that fish have been dying en masse, and are concerned about the implications for the health of the river. In addition, these fish are of high cultural significance to Indigenous communities in the region, including those holding Native Title rights. In response to the first two kills, the Academy was requested by the Leader of the Opposition, the Hon. Bill Shorten MP to provide advice on the immediate causes, as well as exacerbating circumstances from water diversions, agricultural runoff or climate change, and to provide recommendations.Australian Academy of Science, Expert Panel: Craig Moritz, Linda Blackall, Jenny Davis, Tim Flannery, Lee Godden, Lesley Head, Sue Jackson, Richard Kingsford, Sarah Wheeler, John William

Rapid literature mapping on the recent use of machine learning for wildlife imagery

Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, and classify animals and their behaviours. Yet, we currently have very few systematic literature surveys on its use in wildlife imagery. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types. We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species. An increasing number of studies have been published in ecology-specific journals indicating the uptake of deep learning to transform the detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation. Our survey, augmented with bibliometric analyses, provides valuable signposts for future studies to resolve and address shortcomings, gaps, and biases

‘Sub-Prime’ Water, Low-Security Entitlements and Policy Challenges in Over-Allocated River Basins: the Case of the Murray–Darling Basin

Environmental policy is often implemented using market instruments. In some cases, including carbon taxing, the links between financial products and the environmental objectives, are transparent. In other cases, including water markets, the links are less transparent. In Australia’s Murray–Darling Basin (MDB), financial water products are known as ‘entitlements’, and are similar to traditional financial products, such as shares. The Australian water market includes ‘Low Security’ entitlements, which are similar to ‘sub-prime’ mortgage bonds because they are unlikely to yield an amount equal to their financial worth. Nearly half the water purchased under the Murray–Darling Basin Plan for environmental purposes is ‘Low Security’. We suggest that the current portfolio of water held by the Australian Government for environmental purposes reflects the mortgage market in the lead-up to the global financial crisis. Banks assumed that the future value of the mortgage market would reflect past trends. Similarly, it is assumed that the future value of water products will reflect past trends, without considering climate change. Historic records of allocations to ‘Low Security’ entitlements in the MDB suggest that, in the context of climate change, the Basin Plan water portfolio may fall short of the target annual average yield of 2075 GL by 511 GL. We recommend adopting finance sector methods including ‘hedging’ ‘Low Security’ entitlements by purchasing an additional 322–2755 GL of ‘Low Security’, or 160–511 GL of ‘High Security’ entitlements. Securing reliable environmental water is a global problem. Finance economics present opportunities for increasing the reliability of environmental flows

Treatment and outcomes of crisis resolution teams: a prospective multicentre study

<p>Abstract</p> <p>Background</p> <p>Crisis resolution teams (CRTs) aim to help patients in acute mental health crises without admitting them to hospital. The aims of this study were to investigate content of treatment, service practice, and outcomes of crises of CRTs in Norway.</p> <p>Methods</p> <p>The study had a multicentre prospective design, examining routine data for 680 patients and 62 staff members of eight CRTs. The clinical staff collected data on the demographic, clinical, and content of treatment variables. The service practices of the staff were assessed on the Community Program Practice Scale. Information on each CRT was recorded by the team leaders. The outcomes of crises were measured by the changes in Global Assessment of Functioning scale scores and the total scores on the Health of the Nation Outcome Scales between admission and discharge. Regression analysis was used to predict favourable outcomes.</p> <p>Results</p> <p>The mean length of treatment was 19 days for the total sample (N = 680) and 29 days for the 455 patients with more than one consultation; 7.4% of the patients had had more than twice-weekly consultations with any member of the clinical staff of the CRTs. A doctor or psychologist participated in 55.5% of the treatment episodes. The CRTs collaborated with other mental health services in 71.5% of cases and with families/networks in 51.5% of cases. The overall outcomes of the crises were positive, with a small to medium effect size. Patients with depression received the longest treatments and showed most improvement of crisis. Patients with psychotic symptoms and substance abuse problems received the shortest treatments, showed least improvement, and were most often referred to other parts of the mental health services. Length of treatment, being male and single, and a team focus on out-of-office contact were predictors of favourable outcomes of crises in the adjusted model.</p> <p>Conclusions</p> <p>Our study indicates that, compared with the UK, the Norwegian CRTs provided less intensive and less out-of-office care. The Norwegian CRTs worked more with depression and suicidal crises than with psychoses. To be an alternative to hospital admission, the Norwegian CRTs need to intensify their treatment and meet more patients outside the office.</p

Jerarca: Efficient Analysis of Complex Networks Using Hierarchical Clustering

Background: How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use. Methodology/Principal Findings: We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized. Conclusions/Significance: The four main advantages of Jerarca in respect to UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluatio