Petra M. Kuhnert

Eliciting Expert Knowledge in Conservation Science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.

The power of expert opinion in ecological models using Bayesian methods: impact of grazing on birds

One of our greatest challenges as researchers is predicting impacts of land use on biota, and predicting the impact of livestock grazing on birds is no exception. Insufficient data and poor survey design often yield results that are not statistically sig- nificant or that are difficult to interpret because researchers cannot disentangle the effects of grazing from other disturbances. This has resulted in few publications on the impact of grazing on birds alone. Ecologists with extensive experience in bird ecology in grazed landscapes could inform an analysis when time and monetary constraints limit the amount of data that can be collected. Using responses from 20 well-recognized ecologists throughout Australia, we captured this expert knowledge and incorporated it into a statistical model using Bayesian methods. Although relatively new to ecology, Bayesian methods allow straightforward probability statements to be made about specific models or scenarios and the integration of different types of information, including scientific judgment, while formally accom- modating and incorporating the uncertainty in the information provided. Data on bird density were collected across three broad levels of grazing (no/low, mod- erate, and high) typical of subtropical Australia. These field data were used in conjunction with expert data to produce estimates of species persistence under grazing. The addition of expert data through priors in our model strengthened results under at least one grazing level for all but one bird species examined. When experts were in agreement credible intervals were tightened substantially, whereas, when experts were in disagreement, results were similar to those evaluated in the absence of expert information. In fields where there is extensive expert knowledge, yet little published data, the use of expert information as priors for ecological models is a cost-effective way of making more confident predictions about the effect of management on biodiversity.

Statistical power of detecting trends in total suspended sediment loads to the Great Barrier Reef

The export of pollutant loads from coastal catchments is of primary interest to natural resource management. For example, Reef Plan, a joint initiative by the Australian Government and the Queensland Government, has indicated that a 20% reduction in sediment is required by 2020. There is an obvious need to consider our ability to detect any trend if we are to set realistic targets or to reliably identify changes to catchment loads. We investigate the number of years of monitoring aquatic pollutant loads necessary to detect trends. Instead of modelling the trend in the annual loads directly, given their strong relationship to flow, we consider trends through the reduction in concentration for a given flow. Our simulations show very low power (<40%) of detecting changes of 20% over time periods of several decades, indicating that the chances of detecting trends of reasonable magnitudes over these time frames are very small.

Setting the stage for a global-scale trophic analysis of marine top predators: a multi-workshop review

Global-scale studies of marine food webs are rare, despite their necessity for examining and understanding ecosystem level effects of climate variability. Here we review the progress of an international collaboration that compiled regional diet datasets of multiple top predator fishes from the Indian, Pacific and Atlantic Oceans and developed new statistical methods that can be used to obtain a comprehensive ocean-scale understanding of food webs and climate impacts on marine top predators. We loosely define top predators not as species at the apex of the food web, but rather a guild of large predators near the top of the food web. Specifically, we present a framework for world-wide compilation and analysis of global stomach-contents and stable-isotope data of tunas and other large pelagic predatory fishes. To illustrate the utility of the statistical methods, we show an example using yellowfin tuna in a “test” area in the Pacific Ocean. Stomach-contents data were analyzed using a modified (bagged) classification tree approach, which is being prepared as an R statistical software package. Bulk δ15N values of yellowfin tuna muscle tissue were examined using a Generalized Additive Model, after adjusting for spatial differences in the δ15N values of the baseline primary producers predicted by a global coupled ocean circulation-biogeochemical-isotope model. Both techniques in tandem demonstrated the capacity of this approach to elucidate spatial patterns of variations in both forage species and predator trophic positions and have the potential to predict responses to climate change. We believe this methodology could be extended to all marine top predators. Our results emphasize the necessity for quantitative investigations of global-scale datasets when evaluating changes to the food webs underpinning top ocean predators under long-term climatic variability.

Reliability Measures for Local Nodes Assessment in Classification Trees

Most of the modern developments with classification trees are aimed at improving their predictive capacity.This article considers a curiously neglected aspect of classification trees, namely the reliability of predictions that come from a given classification tree. In the sense that a node of a tree represents a point in the predictor space in the limit, the aim of this article is the development of localized assessment of the reliability of prediction rules. A classification tree may be used either to provide a probability forecast, where for each node the membership probabilities for each class constitutes the prediction, or a true classification where each new observation is predictively assigned to a unique class. Correspondingly, two types of reliability measure will be derived—namely, prediction reliability and classification reliability. We use bootstrapping methods as the main tool to construct these measures. We also provide a suite of graphical displays by which they may be easily appreciated. In addition to providing some estimate of the reliability of specific forecasts of each type, these measures can also be used to guide future data collection to improve the effectiveness of the tree model. The motivating example we give has a binary response, namely the presence or absence of a species of Eucalypt, Eucalyptus cloeziana, at a given sampling location in response to a suite of environmental covariates, (although the methods are not restricted to binary response data).

Making management decisions in the face of uncertainty: a case study using the Burdekin catchment in the Great Barrier Reef

Modelling and monitoring pollutants entering into the Great Barrier Reef (GBR) lagoon remain important priorities for the Australian and Queensland governments. Uncertainty analysis of pollutant load delivery to the GBR would: (1) inform decision makers on their ability to meet environmental targets; (2) identify whether additional measurements are required to make confident decisions; and (3) determine whether investments into remediation activities are actually making a difference to water quality and the health of the GBR. Using a case study from the Upper Burdekin catchment where sediment concentrations are the focus, herein we explore and demonstrate different ways of communicating uncertainty to a decision maker. In particular, we show how exceedance probabilities can identify hot spots for future monitoring or remediation activities and how they can be used to inform target setting activities. We provide recommendations for water quality specialists that allow them to make more informed and scientifically defensible decisions that consider uncertainty in both the monitoring and modelling data, as well as allowing the calculation of exceedances from a threshold.