Uncertainty,complexity and controversy in dolphin threat management: A role for post-normal science?

Managing fishing threats to populations of endemic, threatened Hector’s and Māui dolphins around New Zealand is a complex and controversial issue, underpinned by uncertain scientific knowledge. As such, it can be argued that it falls into the realm of post-normal science, which advocates transparency about uncertainties and stakeholder peer review of knowledge feeding into decision-making. This paper focuses on selected examples of modelling and risk assessment research relating to Hector’s and Māui dolphin threat management. It explores how knowledge is developed, shared and utilised by decision-makers, finding that uncertain scientific knowledge may be shared in ways that make it appear more certain, with some of the subjectivities involved in knowledge production hidden from view. Interviews with stakeholders illustrate how some stakeholders are aware of the subjectivities involved when uncertain knowledge underpins decision-making, so a lack of transparency may be leading to erosion of social trust in decisions made. This in turn can lead to a lack of support for dolphin conservation measures from key stakeholders such as the commercial fishing industry. The paper concludes that while moves towards increasing transparency and stakeholder involvement are apparent, a deeper embrace of post-normal science approaches to knowledge production and dissemination would contribute to effective dolphin threat management in New Zealand.     

Assessing the efficiency of Chilean water and sewerage companies accounting for uncertainty

Efficiency assessment of water and sewerage companies (WaSCs) has attracted considerable attention both for water company managers and water regulators. Within the methodological approaches that can be applied to estimate efficiency scores, data envelopment analysis (DEA) is the most widely applied technique. In spite of the positive features of DEA, it presents a major drawback which is its deterministic nature. In other words, conventional DEA models do not account for uncertainty in the data. To overcome this limitation, we assess, for the first time, the efficiency of a sample of Chilean WaSCs by using a DEA model with statistical tolerance in the data. Hence, 81 efficiency scores are estimated for each WaSC rather than a single score as with conventional DEA models. The results illustrate that outputs exhibit larger uncertainty than inputs. Moreover, WaSCs efficiency scores change significantly under the best-case and worst-case scenarios. The ranking of the WaSCs allows for the identification of which of them has the highest performance based on their efficiency scores. This information is essential to enhance efficiency and innovation in the water industry. Moreover, the introduction of uncertainty in the efficiency assessment allows for the prediction and ranking of future performance of WaSCs.     

A dynamic hydrological Monte Carlo simulation model to inform decision-making at Lake Toolibin,Western Australia

Lake Toolibin, an ephemeral lake in the agricultural zone of Western Australia, is under threat from secondary salinity due to land clearance throughout the catchment. The lake is extensively covered with native vegetation and is a Ramsar listed wetland, being one of the few remaining significant migratory bird habitats in the region. Currently, inflow with salinity greater than 1000 mg/L TDS is diverted from the lake in an effort to protect sensitive lakebed vegetation. However, this conservative threshold compromises the frequency and extent of lake inundation, which is essential for bird breeding. It is speculated that relaxing the threshold to 5000 mg/L may pose negligible additional risk to the condition of lakebed vegetation. To characterise the magnitude of improvement in the provision of bird breeding habitat that might be generated by relaxing the threshold, a dynamic water and salt balance model of the lake was developed and implemented using Monte Carlo simulation. Results from best estimate model inputs indicate that relaxation of the threshold increases the likelihood of satisfying habitat requirements by a factor of 9.7. A second-order Monte Carlo analysis incorporating incertitude generated plausible bounds of [2.6, 37.5] around the best estimate for the relative likelihood of satisfying habitat requirements. Parameter-specific sensitivity analyses suggest the availability of habitat is most sensitive to pan evaporation, lower than expected inflow volume, and higher than expected inflow salt concentration. The characterisation of uncertainty associated with environmental variation and incertitude allows managers to make informed risk-weighted decisions.     

Bottom-up uncertainty estimates of global ammonia emissions from global agricultural production systems

Here we present an uncertainty analysis of NH₃ emissions from agricultural production systems based on a global NH₃ emission inventory with a 5×5 min resolution. Of all results the mean is given with a range (10% and 90% percentile). The uncertainty range for the global NH₃ emission from agricultural systems is 27–38 (with a mean of 32) Tg NH₃-N yr⁻¹, N fertilizer use contributing 10–12 (11) Tg yr⁻¹ and livestock production 16–27 (21) Tg yr⁻¹. Most of the emissions from livestock production come from animal houses and storage systems (31–55%); smaller contributions come from the spreading of animal manure (23–38%) and grazing animals (17–37%). This uncertainty analysis allows for identifying and improving those input parameters with a major influence on the results. The most important determinants of the uncertainty related to the global agricultural NH₃ emission comprise four parameters (N excretion rates, NH₃ emission rates for manure in animal houses and storage, the fraction of the time that ruminants graze and the fraction of non-agricultural use of manure) specific to mixed and landless systems, and total animal stocks. Nitrogen excretion rates and NH₃ emission rates from animal houses and storage systems are shown consistently to be the most important parameters in most parts of the world. Input parameters for pastoral systems are less relevant. However, there are clear differences between world regions and individual countries, reflecting the differences in livestock production systems.     

Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers probability safety requirement.     

Uncertainty Analysis In Dissolved Oxygen Modeling in Streams

Uncertainty analysis in surface water quality modeling is an important issue. This paper presents a method based on the first-order reliability method (FORM) to assess the exceedance probability of a target dissolved oxygen concentration in a stream, using a Streeter–Phelps prototype model. Basic uncertainty in the input parameters is considered by representing them as random variables with prescribed probability distributions. Results obtained from FORM analysis compared well with those of the Monte Carlo simulation method. The analysis also presents the stochastic sensitivity of the probabilistic outcome in the form of uncertainty importance factors, and shows how they change with changing simulation time. Furthermore, a parametric sensitivity analysis was conducted to show the effect of selection of different probability distribution functions for the three most important parameters on the design point, exceedance probability, and importance factors. Note: This version was published online in June 2005 with the cover date of August 2004.     

Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.     

Modeling human-induced climatic change: A summary for environmental managers

The rapid increase in atmospheric concentrations of greenhouse gases has caused concern because of their potential to alter the earth's radiation budget and disrupt current climate patterns While there are many uncertainties associated with use of general circulation models (GCMs), GCMs are currently the best available technology to project changes in climate associated with elevated gas concentrations. Results indicate increases in global temperature and changes in global precipitation patterns are likely as a result of doubled CO₂. GCMs are not reliable for use at the regional scale because local scale processes and geography are not taken into account. Comparison of results from five GCMs in three regions of the United States indicate high variability across regions and among models depending on season and climate variable. Statistical methods of scaling model output and nesting finer resolution models in global models are two techniques that may improve projections. Despite the many limitations in GCMs, they are useful tools to explore climate-earth system dynamics when used in conjunction with water resource and ecosystem models. A variety of water resource models showed significant alteration of regional hydrology when run with both GCM-generated and hypothetical climate scenarios, regardless of region or model complexity. Similarly, ecological models demonstrate the sensitivity of ecosystem production, nutrient dynamics, and distribution to changes in climate and CO₂ levels. We recommend the use of GCM-based scenarios in conjunction with water resource and ecosystem models to guide environmental management and policy in a “no-regrets” framework or as part of a precautionary approach to natural resource protection.     

Does taxation alter exploration? : The effect of uncertainty and risk

The effect of resource rent taxation on mineral exploration is a controversial issue on which very little research has been carried out. Simple numerical examples are used in this paper to demonstrate that a ‘pure’ resource rent tax, or Brown Tax, can reduce the extent of exploration of a ‘promising’ deposit by a risk averse explorer, but encourage exploration of ‘unpromising’ deposits. This counter-intuitive result is explained in terms of the effect of the tax and of exploration on the costs of risk and uncertainty.     

Is adaptive co-management ethical?

‘Good’ governance and adaptive co-management hold broad appeal due to their positive connotations and ‘noble ethical claims’. This paper poses a fundamental question: is adaptive co-management ethical? In pursuing an answer to this question, the concept of adaptive co-management is succinctly summarized and three ethical perspectives (deontology, teleology and existentialism) are explored. The case of adaptive co-management in Cambodia is described and subsequently considered through the lens of ethical triangulation. The case illuminates important ethical considerations and directs attention towards the need for meditative thinking which increases the value of tradition, ecology, and culture. Giving ethics a central position makes clear the potential for adaptive co-management to be an agent for governance, which is good, right and authentic as well as an arena to embrace uncertainty.