The 10 Australian ecosystems most vulnerable to tipping points

We identify the 10 major terrestrial and marine ecosystems in Australia most vulnerable to tipping points, in which modest environmental changes can cause disproportionately large changes in ecosystem properties. To accomplish this we independently surveyed the coauthors of this paper to produce a list of candidate ecosystems, and then refined this list during a 2-day workshop. The list includes (1) elevationally restricted mountain ecosystems, (2) tropical savannas, (3) coastal floodplains and wetlands, (4) coral reefs, (5) drier rainforests, (6) wetlands and floodplains in the Murray-Darling Basin, (7) the Mediterranean ecosystems of southwestern Australia, (8) offshore islands, (9) temperate eucalypt forests, and (10) salt marshes and mangroves. Some of these ecosystems are vulnerable to widespread phase-changes that could fundamentally alter ecosystem properties such as habitat structure, species composition, fire regimes, or carbon storage. Others appear susceptible to major changes across only part of their geographic range, whereas yet others are susceptible to a large-scale decline of key biotic components, such as small mammals or stream-dwelling amphibians. For each ecosystem we consider the intrinsic features and external drivers that render it susceptible to tipping points, and identify subtypes of the ecosystem that we deem to be especially vulnerable. © 2011 Elsevier Ltd.

Taxonomic resolution and quantification of freshwater macroinvertebrate samples from an Australian dryland river : The benefits and costs of using species abundance data

In studies using macroinvertebrates as indicators for monitoring rivers and streams, species level identifications in comparison with lower resolution identifications can have greater information content and result in more reliable site classifications and better capacity to discriminate between sites, yet many such programmes identify specimens to the resolution of family rather than species. This is often because it is cheaper to obtain family level data than species level data. Choice of appropriate taxonomic resolution is a compromise between the cost of obtaining data at high taxonomic resolutions and the loss of information at lower resolutions. Optimum taxonomic resolution should be determined by the information required to address programme objectives. Costs saved in identifying macroinvertebrates to family level may not be justified if family level data can not give the answers required and expending the extra cost to obtain species level data may not be warranted if cheaper family level data retains sufficient information to meet objectives. We investigated the influence of taxonomic resolution and sample quantification (abundance vs. presence/absence) on the representation of aquatic macroinvertebrate species assemblage patterns and species richness estimates. The study was conducted in a physically harsh dryland river system (Condamine-Balonne River system, located in south-western Queensland, Australia), characterised by low macroinvertebrate diversity. Our 29 study sites covered a wide geographic range and a diversity of lotic conditions and this was reflected by differences between sites in macroinvertebrate assemblage composition and richness. The usefulness of expending the extra cost necessary to identify macroinvertebrates to species was quantified via the benefits this higher resolution data offered in its capacity to discriminate between sites and give accurate estimates of site species richness. We found that very little information (<6%) was lost by identifying taxa to family (or genus), as opposed to species, and that quantifying the abundance of taxa provided greater resolution for pattern interpretation than simply noting their presence/absence. Species richness was very well represented by genus, family and order richness, so that each of these could be used as surrogates of species richness if, for example, surveying to identify diversity hot-spots. It is suggested that sharing of common ecological responses among species within higher taxonomic units is the most plausible mechanism for the results. Based on a cost/benefit analysis, family level abundance data is recommended as the best resolution for resolving patterns in macroinvertebrate assemblages in this system. The relevance of these findings are discussed in the context of other low diversity, harsh, dryland river systems.

Queensland farmers rise up to take on the miners

Queensland’s Surat Basin has the third largest energy resource in the world — with vast coal seam gas and coal reserves — but farm groups are warning that mining areas, which are prime farm land, risk catastrophic environmental damage to food-producing areas. Mining development is moving very quickly with 36,000 wells due to be sunk in the next few years. A Senate inquiry into the impacts of mining in the Murray-Darling Basin heard evidence from farm and mining industry representatives in Oakey on the Darling Downs yesterday.

Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance.

Decision-making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream-flows in north-eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators’ attitude towards risk.

Formative evaluation to benchmark and improve climate-based decision support for graziers in Western Queelsland

Researchers developing climate-based forecasts, workshops, software tools and information to aid grazier decisions undertook an evaluation study to enhance planning and benchmark impact. One hundred graziers in Western Queensland were randomly selected from 7 shires and surveyed by mail and telephone (43 respondents) to explore levels of knowledge and use of climate information, practices and information needs. We found 36% of respondents apply the Southern Oscillation Index to property decisions but 92% were unaware El Niño Southern Oscillation’s predictive signal in the region is greater for pasture growth than rainfall, suggesting they may not recognise the potential of pasture growth forecasts. Almost 75% of graziers consider they are conservative or risk averse in their attitude to managing their enterprise. Mail respondents (n= 20) if given a 68%, on average, probability of exceeding median rainfall forecast may change a decision; almost two-thirds vary stocking rate based on forage available, last year’s pasture growth or the Southern Oscillation Index; the balance maintain a constant stocking rate strategy; 90% have access to a computer; 75% to the internet and 95% have a fax. This paper presents findings of the study and draws comparisons with a similar study of 174 irrigators in the Northern Murray-Darling Basin (Aust. J. Exp. Ag. 44, 247-257). New insights and information gained are helping the team better understand client needs and plan, design and extend tools and information tailored to grazier knowledge, practice, information needs and preferences. Results have also provided a benchmark against which to measure project impact and have influenced the team to make important changes to their project planning, activities and methods for transferring technology tailored to grazier preferences.

Rainfall and streamflow response to El Niño Southern Oscillation: a case study in a semiarid catchment, Australia.

This paper aims to compare the shift in frequency distribution and skill of seasonal climate forecasting of both streamflow and rainfall in eastern Australia based on the Southern Oscillation Index (SOI) Phase system. Recent advances in seasonal forecasting of climate variables have highlighted opportunities for improving decision making in natural resources management. Forecasting of rainfall probabilities for different regions in Australia is available, but the use of similar forecasts for water resource supply has not been developed. The use of streamflow forecasts may provide better information for decision-making in irrigation supply and flow management for improved ecological outcomes. To examine the relative efficacy of seasonal forecasting of streamflow and rainfall, the shift in probability distributions and the forecast skill were evaluated using the Wilcoxon rank-sum test and the linear error in probability space (LEPS) skill score, respectively, at three river gauging stations in the Border Rivers Catchment of the Murray-Darling Basin in eastern Australia. A comparison of rainfall and streamflow distributions confirms higher statistical significance in the shift of streamflow distribution than that in rainfall distribution. Moreover, streamflow distribution showed greater skill of forecasting with 0-3 month lead time, compared to rainfall distribution.

Meso-scale movement patterns of native fish

The Murray Darling Basin Commison sought information on the movement patterns of native fish in the Murray Darling River system in Queensland. Information is needed to determine daily movement patterns, movement direction and results of flow event analysis.

Improved methodology for introducing threatened hatchery reared fish

Eel tailed catfish, silver perch and Murray cod are three key recreational fishing species that have declined in the Murray-Darling Basin region. This research project will be an important step towards developing methods to restore and enhance stocks of these fish.

Water Use Efficiency Investment for Healthy Headwaters

Agri-Science Queensland to provide a range of extension services to improve water use efficiency in irrigated agriculture in the Queensland Murray Darling Basin and support the implementation of the Healthy Headwaters On-farm Water Use Efficiency program.

Demonstration reaches: Looking back whilst moving forward with river rehabilitation under the Native Fish Strategy

‘Demonstration reaches’ are sections of river where multiple threats to native fish are addressed through river rehabilitation and strong community participation. They are an important way of promoting the key driving actions of the Murray-Darling Basin Authority's Native Fish Strategy (NFS) by using on-ground community-driven rehabilitation. Measuring rehabilitation success against well-defined targets and using this information to adaptively mange activities is fundamental to the demonstration reach philosophy. Seven years on from the establishment of the first demonstration reach, there are now seven throughout the Murray-Darling Basin (MDB), all in differing states of maturation and but all applying a standardised framework for monitoring native fish outcomes. In this study, we reflect on the role that demonstration reaches have played within the NFS, synthesise some key findings from 32 monitoring and evaluation outputs, and highlight some of the successes and barriers to success. We make recommendations as to how to strengthen the demonstration reach model to ensure it remains a relevant approach for fish habitat rehabilitation beyond the NFS and MDB.

Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification

Clustering of multivariate data is a commonly used technique in ecology, and many approaches to clustering are available. The results from a clustering algorithm are uncertain, but few clustering approaches explicitly acknowledge this uncertainty. One exception is Bayesian mixture modelling, which treats all results probabilistically, and allows comparison of multiple plausible classifications of the same data set. We used this method, implemented in the AutoClass program, to classify catchments (watersheds) in the Murray Darling Basin (MDB), Australia, based on their physiographic characteristics (e.g. slope, rainfall, lithology). The most likely classification found nine classes of catchments. Members of each class were aggregated geographically within the MDB. Rainfall and slope were the two most important variables that defined classes. The second-most likely classification was very similar to the first, but had one fewer class. Increasing the nominal uncertainty of continuous data resulted in a most likely classification with five classes, which were again aggregated geographically. Membership probabilities suggested that a small number of cases could be members of either of two classes. Such cases were located on the edges of groups of catchments that belonged to one class, with a group belonging to the second-most likely class adjacent. A comparison of the Bayesian approach to a distance-based deterministic method showed that the Bayesian mixture model produced solutions that were more spatially cohesive and intuitively appealing. The probabilistic presentation of results from the Bayesian classification allows richer interpretation, including decisions on how to treat cases that are intermediate between two or more classes, and whether to consider more than one classification. The explicit consideration and presentation of uncertainty makes this approach useful for ecological investigations, where both data and expectations are often highly uncertain.

Differential macrohabitat use by birds on an unregulated lowland floodplain of south-eastern Australia

In many lowland floodplains around the world, upriver interferences to flows (weirs, dams, off-takes) have led to much reduced frequency and duration of flooding. As a result, many floodplain wetlands are now inundated relatively rarely if at all. Given regulation of most lowland rivers in southeastern Australia, we assessed use of wetlands by birds in the essentially unregulated Ovens River in northeastern Victoria. Twelve sites (0.4-1.2 ha) were studied after flooding. Four sites were 'permanent billabongs', four were temporary wetlands and the other four were randomly selected woodland sites >60 m from the nearest water body (including the river) acting as 'control' or 'reference' sites. Aquatic birds were not recorded using woodland sites, but many species were differentially associated with either billabongs or temporary wetlands. A surprising number of non-aquatic birds either exclusively or differentially were associated with wetland sites compared with woodland sites. We concluded that heterogeneous macrohabitat will increase local avian biodiversity on lowland floodplains. Moreover, densities and diversity of non-aquatic, woodland species also increased with the presence of wetlands. Temporary wetlands were used differently from permanent billabongs by birds, especially in foraging methods. This suggests that the reinstatement of major flooding on heavily regulated floodplains would be ecologically advantageous for birds by providing foraging and breeding opportunities.

Temporal changes in macroinvertebrate assemblages following experimental flooding in permanent and temporary wetlands in an Australian floodplain forest

The River Murray, Australia, is a highly regulated river from which almost 80% of mean annual flow is removed for human use, primarily irrigated agriculture. Consequent changes to the pattern and volume of river flow are reflected in floodplain hydrology and, therefore, the wetting/drying patterns of floodplain wetlands. To explore the significance of these changes, macroinvertebrate samples were compared between permanent and temporary wetlands following experimental flooding in a forested floodplain of the River Murray. Weekly samples from two permanent wetlands and four associated temporary sites were used to track changes in macroinvertebrate assemblage composition. Non-metric multidimensional scaling was used to ordinate the macroinvertebrate data, indicating consistent differences between the biota of permanent and temporary wetlands and between the initial and later assemblages in the temporary sites. There were marked changes over time, but little sign that the permanent and temporary assemblages were becoming more alike over the 25-week observation period. The apparent heterogeneity of these systems is of particular importance in developing river management plans which are likely to change flooding patterns. Such plans need to maintain a mosaic of wetland habitats if floodplain biodiversity is to be supported.

Bayesian classification of catchments using spatial data: a first step to improved modelling of catchment effects on stream ecological condition

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

Bearing the risk : learning to be drier mid-river

This paper investigates learning related to the phenomena of drying over the past decade in the southern Murray-Darling Basin in Australia, as perceived in a mid-river site within the western Riverina of New South Wales, Australia. The insights from audio-recorded interviews, with a wide range of adults across the water-dependent community, mostly relate to the catchment of the  Murrumbidgee River in the Shire of Hay. Our overarching theme is about how  people are learning about, understanding and bearing the risks, of what is widely regarded as a prolonged drought. For some, the learning is about how to cope with less water in the Basin, and particularly from the river, as predicted in the climate change literature. Our narrative-based, empirical research registers the felt experience of those located, in situ, as a severe ‘irrigation drought’ extends into 2009. The paper dramatises the many obstacles to learning how to think and act differently, in difficult and rapidly changing ecosocial circumstances.