Thermoluminescence of zircon: a kinetic model

The mineral zircon, ZrSiO4, belongs to a class of promising materials for geochronometry by means of thermoluminescence (TL) dating. The development of a reliable and reproducible method for TL dating with zircon requires detailed knowledge of the processes taking place during exposure to ionizing radiation, long-term storage, annealing at moderate temperatures and heating at a constant rate (TL measurements). To understand these processes one needs a kinetic model of TL. This paper is devoted to the construction of such amodel. The goal is to study the qualitative behaviour of the system and to determine the parameters and processes controlling TL phenomena of zircon. The model considers the following processes: (i) Filling of electron and hole traps at the excitation stage as a function of the dose rate and the dose for both (low dose rate) natural and (high dose rate) laboratory irradiation. (ii) Time dependence of TL fading in samples irradiated under laboratory conditions. (iii) Short time annealing at a given temperature. (iv) Heating of the irradiated sample to simulate TL experiments both after laboratory and natural irradiation. The input parameters of the model, such as the types and concentrations of the TL centres and the energy distributions of the hole and electron traps, were obtained by analysing the experimental data on fading of the TL-emission spectra of samples from different geological locations. Electron paramagnetic resonance (EPR) data were used to establish the nature of the TL centres. Glow curves and 3D TL emission spectra are simulated and compared with the experimental data on time-dependent TL fading. The saturation and annealing behaviour of filled trap concentrations has been considered in the framework of the proposed kinetic model and comparedwith the EPR data associated with the rare-earth ions Tb3+ and Dy3+, which play a crucial role as hole traps and recombination centres. Inaddition, the behaviour of some of the SiOmn− centres has been compared with simulation results.

On the importance of grain size in luminescence dating using quartz

There are two major problems commonly encountered when applying Optically Stimulated Luminescence (OSL) dating in the high dose range: (i) age discrepancy between different grain sizes, and (ii) age underestimation. A marked and systematic discrepancy between fine-grain (4–11 μm) and coarse-grain (63–90 μm) quartz single aliquot regeneration protocol (SAR) ages has been reported previously for Romanian and Serbian loess >40 ka (De of ∼100 Gy), generally with fine-grain ages underestimating the depositional age. In this paper, we show a similar age pattern for two grain size fractions from Chinese loess, thus pointing to a potential worldwide phenomenon. While age underestimation is often attributed to signal saturation problems, this is not the case for fine grain material, which saturates at higher doses than coarse grains, yet begins to underestimate true ages earlier. Here we examine the dose response curves of quartz from different sedimentary contexts around the world, using a range of grain sizes (diameters of 4–11 μm, 11–30 μm, 35–50 μm, 63–90 μm, 90–125 μm, 125–180 μm, and 180–250 μm). All dose response curves can be adequately described by a sum of two saturating exponential functions, whose saturation characteristics (D0 values) are clearly anticorrelated with grain diameter (φ) through an inverse square root relationship, D0 = A/√φ, where A is a scaling factor. While the mechanism behind this grain-size dependency of saturation characteristics still needs to be understood, our results show that the observation of an extended SAR laboratory dose response curve does not necessarily enable high doses to be recorded accurately, or provide a corresponding extended age range

A framework for improving the cross-jurisdictional governance of a marine migratory species

Marine migratory species require collaborative decision-making because individuals move across jurisdictional boundaries within and between countries. However, governance of these species is not always harmonized or truly collaborative. We analyzed the Recovery Plan for Marine Turtles in Australia 2017 (the Plan) and three of its subsidiary plans for evidence of collaborative governance using a two part gap analysis and interviews with environmental managers, scientists, and other stakeholders involved in the development of the Plan and in managing marine migratory species in Australia more generally. We applied existing adaptive and collaborative governance frameworks, which focused mainly on the social components of collaborative governance, and identified a need for a new, interdisciplinary framework for the collaborative governance of marine turtles in Australia. We applied our new framework to the Plan and identified that while the biological components of the Plan were well-developed, stakeholder analysis and engagement details were largely missing. We recognize that recovery plans are inevitably silent about certain issues but suggest that plans would benefit from including better guidance on stakeholder engagement and analysis. Our framework is directly relevant to harmonizing the management of marine turtles across jurisdictions in Australia but it could also be applied to managing threats towards other migratory species that inhabit large marine jurisdictions

Active adaptive conservation of threatened species in the face of uncertainty

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives