Evan Harrison

Analyzing cause and effect in environmental assessments: using weighted evidence from the literature

Abstract.  Sound decision making in environmental research and management requires an understanding of causal relationships between stressors and ecological responses. However, demonstrating cause–effect relationships in natural systems is challenging because of difficulties with natural variability, performing experiments, lack of replication, and the presence of confounding influences. Thus, even the best-designed study may not establish causality. We describe a method that uses evidence available in the extensive published ecological literature to assess support for cause–effect hypotheses in environmental investigations. Our method, called Eco Evidence, is a form of causal criteria analysis—a technique developed by epidemiologists in the 1960s—who faced similar difficulties in attributing causation. The Eco Evidence method is an 8-step process in which the user conducts a systematic review of the evidence for one or more cause–effect hypotheses to assess the level of support for an overall question. In contrast to causal criteria analyses in epidemiology, users of Eco Evidence use a subset of criteria most relevant to environmental investigations and weight each piece of evidence according to its study design. Stronger studies contribute more to the assessment of causality, but weaker evidence is not discarded. This feature is important because environmental evidence is often scarce. The outputs of the analysis are a guide to the strength of evidence for or against the cause–effect hypotheses. They strengthen confidence in the conclusions drawn from that evidence, but cannot ever prove causality. They also indicate situations where knowledge gaps signify insufficient evidence to reach a conclusion. The method is supported by the freely available Eco Evidence software package, which produces a standard report, maximizing the transparency and repeatability of any assessment. Environmental science has lagged behind other disciplines in systematic assessment of evidence to improve research and management. Using the Eco Evidence method, environmental scientists can better use the extensive published literature to guide evidence-based decisions and undertake transparent assessments of ecological cause and effect.

Discretization of continuous predictor variables in Bayesian networks

Bayesian networks (BNs) are a popular tool in natural resource management but are limited when dealing with ecological assemblage data and when discretizing continuous variables. We present a method that addresses these challenges using a BN model developed for the Upper Murrumbidgee River Catchment (south-eastern Australia). A selection process was conducted to choose the taxa from the whole macroinvertebrate assemblage that were incorporated in the BN as endpoints. Furthermore, two different approaches to the discretization of continuous predictor variables for the BN were compared. One approach used Threshold Indicator Taxa Analysis (TITAN) which estimates the thresholds based on the biological community. The other approach used was the expert opinion. The TITAN-based discretizations provided comparable predictions to expert opinion-based discretizations but in combining statistical rigor and ecological relevance, offer a novel and objective approach to the discretization. The TITAN-based method may be used together with expert opinion. We propose the empirical estimation of thresholds to discretize continuous predictor variables within Bayesian networks.We used a case study to illustrate it.Predefined criteria were used to select five macroinvertebrate taxa that were incorporated in the BN as endpoints.Continuous predictor variables were discretized using Threshold Indicator Taxa Analysis (TITAN).TITAN-based discretizations provided comparable predictions to expert opinion-based discretizations.

The imperative need for nationally coordinated bioassessment of rivers and streams

Declining water quality and ecological condition is a typical trend for rivers and streams worldwide as human demands for water resources increase. Managing these natural resources sustainably is a key responsibility of governments. Effective water management policies require information derived from long-term monitoring and evaluation. Biological monitoring and assessment are critical for management because bioassessment integrates the biological, physical and chemical features of a waterbody. Investment in nationally coordinated riverine bioassessment in Australia has almost ceased and the foci of management questions are on more localised assessments. However, rivers often span political and administrative boundaries, and their condition may be best protected and managed under national policies, supported by a coordinated national bioassessment framework. We argue that a nationally coordinated program for the bioassessment of riverine health is an essential element of sustainable management of a nation’s water resources. We outline new techniques and research needed to streamline current arrangements to meet present-day and emerging challenges for coordinating and integrating local, regional and national bioassessment activities. This paper draws on international experience in riverine bioassessment to identify attributes of successful broad-scale bioassessment programs and strategies needed to modernise freshwater bioassessment in Australia and re-establish national broad-scale focus.

Evaluating AUSRIVAS predictive model performance for detecting simulated eutrophication effects on invertebrate assemblages

Abstract: Confidence in any bioassessment method is related to its ability to detect ecological improvement or impairment. We evaluated Australian River Assessment (AUSRIVAS)-style predictive models built using referencesite data sets from the Australian Capital Territory (ACT), the Yukon Territory (YT; Canada), and the Laurentian Great Lakes (GL; North America) area. We evaluated model performance as ability to correctly assign reference condition with independent reference-site data. Evaluating model ability to detect human disturbance is generally more problematic because the actual condition of test sites is usually unknown. Independent reference-site data underwent simulated impairment by varying the proportions of sensitive, intermediate, and tolerant taxa to simulate degrees of eutrophication. Model performance was related to differences in data sets, such as number and distribution of invertebrate taxa. Sensitive taxa tended to have lower expected probabilities of occurrence than more-tolerant taxa, but the distribution of taxa grouped by tolerance categories also differed by data set. Thus, the models differed in ability to detect the simulated impairment. The ACT model performed best with respect to Type 1 error rates (0%) and the GL model the worst (38%). The YT model performed best (10% error) for detecting moderate impairment, and the ACT model detected all severely impaired sites. AUSRIVAS did not assign most mildly impaired sites to below-reference condition, but a reduction in observed/expected values for some of the mildly impaired sites was observed. Models did not detect mild impairment that simply changed taxon abundances because presence—absence data were used for models. However, in comparison with other models described in this special issue (that did use abundance data), the AUSRIVAS model performance was comparable or better for detecting the simulated moderate and severe impairments.

Historical Land-Use Influences the Long-Term Stream Turbidity Response to a Wildfire

Wildfires commonly result in an increase in stream turbidity. However, the influence of pre-fire land-use practices on post-fire stream turbidity is not well understood. The Lower Cotter Catchment (LCC) in south-eastern Australia is part of the main water supply catchment for Canberra with land in the catchment historically managed for a mix of conservation (native eucalypt forest) and pine (Pinus radiata) plantation. In January 2003, wildfires burned almost all of the native and pine forests in the LCC. A study was established in 2005 to determine stream post-fire turbidity recovery within the native and pine forest areas of the catchment. Turbidity data loggers were deployed in two creeks within burned native forest and burned pine forest areas to determine turbidity response to fire in these areas. As a part of the study, we also determined changes in bare soil in the native and pine forest areas since the fire. The results suggest that the time, it takes turbidity levels to decrease following wildfire, is dependent upon the preceding land-use. In the LCC, turbidity levels decreased more rapidly in areas previously with native vegetation compared to areas which were previously used for pine forestry. This is likely because of a higher percentage of bare soil areas for a longer period of time in the ex-pine forest estate and instream stores of fine sediment from catchment erosion during post-fire storm events. The results of our study show that the previous land-use may exert considerable control over on-going turbidity levels following a wildfire.