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Do dams on rivers cause changes in fish assemblages? Does acid mine drainage cause degradation of stream macroinvertebrate assemblages?
Do invading species cause the loss of local biodiversity? In an ideal world the answers to such questions would be self-evident and entirely indisputable. In the real world today, it can sometimes depend who you ask....
“(Many) questions of importance are being asked of environmental sciences today... yet how much confidence can we have in answering them? Although we may see statistical associations between apparent stressors and indicators of environmental degradation, reaching a conclusion with an acceptable level of confidence that one thing actually causes another is challenging in environmental research.” (Norris et al. submitted).
Take a finding of differences between fish assemblages upstream and downstream of a dam. These could be because of the dam itself, or they could be the result of a host of other factors. If no data is available on the pre-development state of the area, if other confounding environmental factors are present or if it proves difficult to allocate control locations, water managers may struggle to positively identify a cause.
Ecological restoration and ecological assessment is thus complicated by the extreme difficulty of drawing firm cause and effect conclusions from environmental investigations.
In some cases, scarcity of data, or the difficulty of drawing unambiguous cause—effect relationships from environmental investigations has been to blame. Studies providing solid results for one region are unlikely to give much confidence to those working elsewhere on different systems, for instance.
In other cases the sheer weight of available scientific literature has hampered decision-making and opened the way to confirmation bias - the very human tendency to favour information that confirms preconceptions or hypotheses and to ignore that which confronts them.
What the world urgently needs, a growing body of scientists believe, is both an open, accessible international database for storing and sharing ecological evidence from the literature, and methods and software capable of analysing that evidence.
Now an international group of scientists and water managers is actively working to devise both of these.
The team is in broad agreement that better sharing of evidence is needed. For instance, ecologists have been slow to embrace data sharing, in spite of opportunities for improved knowledge through the synthesis of data from many studies. Concerns include a lack of accreditation systems, and the fear of having novel conclusions ‘scooped’ by other researchers.
However evidence comprises statements based on data, but is not itself primary data.
It is possible the sharing of existing evidence extracted from published literature will cause far less angst than sharing of primary data (Zeigler and Webb). In part that’s because evidence is drawn from previously published sources, and conclusions highlighted in database entries can’t be misappropriated, with users having to cite the original publication when using such evidence. Thus an ecological evidence database may be a step towards greater acceptance of data sharing by ecologists.
Even the largest database, however, is of little use if it isn’t widely accessible and its evidence universally comprehensible. What’s needed is a common lingo to allow researchers, water managers and scientists around the globe to share and adopt that evidence. Standards setting out terms, definitions and interactions are a globally accepted way of ensuring consistency of nomenclature and relationships of terms and can therefore help to establish a common language.
A partnership of scientists and managers from North America, Europe and Australia is working to establish such a Global Evidence Exchange, and to address issues such as the need to develop suitable content governance, set standards for interoperability between databases and create web technology for sharing evidence. The team also agree on the need to develop a not-for-profit, yet sustainable, business model to ensure that the benefits of a global evidence exchange endure.
Better use of evidence: Causal Criteria and Eco Evidence
The Global Evidence Exchange will not by itself improve the use of evidence in environmental decision making. Equally essential are methods and tools that draw on evidence to support decision making.
In particular there is need for a clear and well-defined method for synthesising the evidence to understand causal relationships between stressors and ecological response. This could have many uses:
Pioneering the field, Professor Richard Norris and eWater colleagues from the universities of Canberra and Melbourne are urging the adoption of ‘causal criteria’. Causal criteria were first adopted in a landmark 1964 report prepared by an advisory committee to the US Surgeon General on the health effects of smoking (USDHEW 1964). In that report, the committee recognised that experimental design issues, such as those described above, mean that statistical methods alone are often insufficient for proving causal relationships, which instead were a matter of judgement. The causal criteria are a series of logical devices designed to aid such judgment.
eWater researchers have developed new software — called Eco Evidence —to help users apply causal criteria in environmental investigations. It allows the analysis of existing evidence in a well-documented, transparent, repeatable and rigorous manner. Eco Evidence can also be used to store evidence from a particular analysis, or ‘published’ to its on-line database by forward-thinking research and management organisations, so it can be re-used or shared with other users.
New habits required
The combination of the Global Evidence Exchange and new decision support tools like Eco Evidence can support evidence-based decision-making for environmental management. However the last step is up to decision makers themselves, to embrace the principle of evidence-based decision making, discard old habits and form new ones based on the novel methods and technology.
Greet J, Webb JA and Cousens RD (2011), The importance of seasonal flow timing for riparian vegetation dynamics: a systematic review using causal criteria analysis. Freshwater Biology:doi:10.1111/j.1365-2427.2011.02564.x
Nichols S, Webb A, Norris R, and Stewardson M (2011) Eco Evidence Analysis methods manual: a systematic approach to evaluate causality in environmental science: ISBN 978-1-921543-43-2 eWater Cooperative Research Centre, Canberra.
Norris RH, Webb JA, Nichols SJ, Stewardson MJ and Harrison ET (submitted 24/03/2011). Analyzing cause and effect in environmental assessments: using weighted evidence from the literature. The Journal of the North American Benthological Society
Wealands SR, Webb JA, and Stewardson MJ (2009) Evidence-based model structure: The role of causal analysis in hydro-ecological modelling. In R. S. Anderssen, R. D. Braddock and L. T. H. Newham (Eds.). 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, pp. 2465-2471. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, Cairns, Australia. Available at: http://tinyurl.com/ybcb3we.
WISER (2010) The Causal Analysis/Diagnosis Decision Information System. WISER Project Newsletter. Waterbodies in Europe: Integrative Systems to Assess Ecological Status and Recovery, pp 2-3. Available at: http://www.wiser.eu/download/WISER_newsletter_issue_04.pdf.