An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world’s ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity

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This paper discusses, from a philosophical perspective, the reasons for considering the power of any statistical test used in environmental biomonitoring. Power is inversely related to the probability of making a Type II error (i.e. low power indicates a high probability of Type II error). In the context of environmental monitoring, a Type II error is made when it is concluded that no environmental impact has occurred even though one has. Type II errors have been ignored relative to Type I errors (the mistake of concluding that there is an impact when one has not occurred), the rates of which are stipulated by the a values of the test. In contrast, power depends on the value of α, the sample size used in the test, the effect size to be detected, and the variability inherent in the data. Although power ideas have been known for years, only recently have these issues attracted the attention of ecologists and have methods been available for calculating power easily. Understanding statistical power gives three ways to improve environmental monitoring and to inform decisions about actions arising from monitoring. First, it allows the most sensitive tests to be chosen from among those applicable to the data. Second, preliminary power analysis can be used to indicate the sample sizes necessary to detect an environmental change. Third, power analysis should be used after any nonsignificant result is obtained in order to judge whether that result can be interpreted with confidence or the test was too weak to examine the null hypothesis properly. Power procedures are concerned with the statistical significance of tests of the null hypothesis, and they lend little insight, on their own, into the workings of nature. Power analyses are, however, essential to designing sensitive tests and correctly interpreting their results. The biological or environmental significance of any result, including whether the impact is beneficial or harmful, is a separate issue. The most compelling reason for considering power is that Type II errors can be more costly than Type I errors for environmental management. This is because the commitment of time, energy and people to fighting a false alarm (a Type I error) may continue only in the short term until the mistake is discovered. In contrast, the cost of not doing something when in fact it should be done (a Type II error) will have both short- and long-term costs (e.g. ensuing environmental degradation and the eventual cost of its rectification). Low power can be disastrous for environmental monitoring programmes.

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The state of global freshwater ecosystems is increasingly parlous with water resource development degrading high-conservation wetlands. Rehabilitation is challenging because necessary increases in environmental flows have concomitant social impacts, complicated because many rivers flow between jurisdictions or countries. Australia’s Murray–Darling Basin is a large river basin with such problems encapsulated in the crisis of its Ramsar-listed terminal wetland, the Coorong, Lower Lakes and Murray Mouth. Prolonged drought and upstream diversion of water dropped water levels in the Lakes below sea level (2009–2010), exposing hazardous acid sulfate soils. Salinities increased dramatically (e.g. South Lagoon of Coorong >200 g L–1, cf. modelled natural 80 g L–1), reducing populations of waterbirds, fish, macroinvertebrates and littoral plants. Calcareous masses of estuarine tubeworms (Ficopomatus enigmaticus) killed freshwater turtles (Chelidae) and other fauna. Management primarily focussed on treating symptoms (e.g. acidification), rather than reduced flows, at considerable expense (>AU$2 billion). We modelled a scenario that increased annual flows during low-flow periods from current levels up to one-third of what the natural flow would have been, potentially delivering substantial environmental benefits and avoiding future crises. Realisation of this outcome depends on increasing environmental flows and implementing sophisticated river management during dry periods, both highly contentious options.

Benthic macroinvertebrates were sampled from four sites on upland streams in the Wentworth Ealls area of the Blue Mountains, NSW. One site received effluent from a sewage treatment plant and the others were reference sites. Eive replicate collections were taken from each site on four occasions at intervals of 3 months. Macroinvertebrate community data were analysed using univariate (ANOVA) and multivariate (NMDS) techniques and comparisons were made between analyses at different levels of taxonomic aggregation and using different methods of data transformation. Similar pattems were observed at both species and family levels, and even the order level showed a clear community response to effluent input. Binary (presence/ absence) data provided similar results to quantitative data for the species and family levels. However, when binary data were used at the order level, the distinctions between the reference sites became blurred. We discuss the implications of these findings for environmental monitoring.

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Summary 1. Indicators are crucial to many socio‐political schemes for portraying environmental influences of society. For example, the OECD model for State of the Environment Reporting uses three different sorts of indicators (pressure, condition, response) to differentiate the present condition of the environment from the anthropogenic pressures upon it and from any societal responses made to alleviate those pressures (thereby improving aspects of the overall condition). 2. These sorts of indicators have a fundamental technical basis in the science supporting their exposition and usage. However, the criteria used in interpreting the indicator values are likely to be set by considerations that go beyond scientific grounds. That is, indicators are socially determined in the end. However, many scientists find it difficult to involve the public in such reporting. 3. Scientists who are uncomfortable with this non‐technical use of their indicator constructs should recognize that it is merely another manifestation of the overall broadening of environmental concern termed ‘ecosystem health’. The emerging field of ecosystem health seeks to take our technical understanding of how the environment functions and combine it with socio‐economic goals, using a human health metaphor and an ethical underpinning. 4. River health might be better served by adopting a veterinary approach rather than the model of human health. This is because, like animals, riverine environments come in many different forms and cannot complain of ill health. Desirable interventions will vary with the uses to which we wish to put a river and our reasons for being concerned about a river’s health. A framework for this diagnostic approach is presented. 5. An enormous challenge lies ahead in integrating the various measurements of riverine attributes that might together constitute ‘river health’. We need ways to cater for the pluralism of modern societies, and we need more dynamic assessments of river condition, possibly founded on studies of key ecological processes.