Rohan Sadler

Spatial scale invariance of southern Australian forest fires mirrors the scaling behaviour of fire-driving weather events

Power law frequency-size distributions of forest fires have been observed in a range of environments. The scaling behaviour of fires, and more generally of landscape patterns related to recurring disturbance and recovery, have previously been explained in the frameworks of self-organized criticality (SOC) and highly optimized tolerance (HOT). In these frameworks the scaling behaviour of the fires is the global structure that either emerges spontaneously from locally operating processes (SOC) or is the product of a tuning process aimed at optimizing the trade-offs between system yield and tolerance to risks (HOT). Here, we argue that the dominant role of self-organized or optimised fuel patterns in constraining unplanned-fire sizes, implicit in the SOC and HOT frameworks, fails to recognise the strong exogenous controls of fire spread (i.e. by weather, terrain, and suppression) observed in many fire-prone landscapes. Using data from southern Australia we demonstrate that forest fire areas and the magnitudes of corresponding weather events have distributions with closely matching scaling exponents. We conclude that the spatial scale invariance of forest fires may also be a mapping of the meteorological forcing pattern.

Error propagation in normalization of stable isotope data: a Monte Carlo analysis

A higher analytical precision of a stable isotope ratio mass spectrometer does not automatically guarantee accurate determination of the true isotope composition (delta-value) of samples, since estimates of true delta-values are obtained from the normalization of raw isotope data. We performed both Monte Carlo simulations and laboratory experiments to investigate aspects of error propagation during the normalization of carbon stable isotope data. We found that increasing both the number of different reference standards and the number of repetitions of each of these standards reduces the normalization error. A 50% reduction in the normalization error can be achieved over the two-point normalization by either analyzing two standards four times each, or four standards two times each. If the true delta-value of a sample is approximately known a priori, the normalization error may then be reduced through a targeted choice of locally optimal standards. However, the difference in improvement is minimal and, therefore, a more practical strategy is to use two or more standards covering the whole stable isotope scale. The selection of different sets of standards by different laboratories or for different batches of samples in the same laboratory may lead to significant differences in the normalized delta-values of the same samples, leading to inconsistent results. Hence, the same set of standards should always be used for a particular element and a particular stable isotope analytical technique.

Optimal conservation investment for a biodiversity-rich agricultural landscape*

This study develops a theoretical and empirical framework for optimal conservation planning using satellite land cover data and economic data from a farm survey. A case study is presented for a region within the South-west Australia Biodiversity Hotspot (Nature 403, 853). This Biodiversity Hotspot is a focus for conservation investment as it combines a relatively high level of biodiversity with severe threat to the biodiversity from agriculture. The conservation planning model developed determines the optimal set of bush fragments for conservation. This model can also be used to assess the trade-off between the budget and a vegetation species metric. Results from the case study show that, without an effective conservation scheme that at least fences fragments, significant plant biodiversity losses will occur in the North East Wheatbelt Regional Organisation of Councils region of the WA wheatbelt over a 10-year period. A perfect price discriminating auction scheme could reduce the costs of conservation by around 17 per cent relative to a fixed-payment scheme; however, a fixed payment on outcome (measured as change in the species metric) scheme represents a viable second-best alternative, to a conservation auction, where conservation spending is spatially targeted.

A strategy for selection of reference materials in stable oxygen isotope analyses of solid materials.

The propagation of uncertainties associated with the stable oxygen isotope reference materials through a multi-point normalisation procedure was evaluated in this study using Monte Carlo (MC) simulation. We quantified the normalisation error for a particular selection of reference materials and their number of replicates, when the choice of standards is restricted to either nitrates, sulphates or organic reference materials alone, and in comparison with when this restriction was relaxed. A lower uncertainty in stable oxygen isotope analyses of solid materials performed using High-Temperature Pyrolysis (HTP) can be readily achieved through an optimal selection of reference materials. Among the currently available certified reference materials the best performing pairs minimising the normalisation errors are USGS35 and USGS34 for nitrates; IAEA-SO-6 and IAEA-SO-5 for sulphates; and IAEA-601 and IAEA-602 for organic materials. The normalisation error can be reduced further--by approximately half--if each of these two analysed reference materials is replicated four times. The overall optimal selection among all nine considered reference materials is the IAEA-602 and IAEA-SO-6 pair. If each of these two reference materials is replicated four times the maximum predicted normalisation error will equal 0.22‰, the minimum normalisation error 0.12‰, and the mean normalisation error 0.15‰ over the natural range of δ(18)O variability. We argue that the proposed approach provides useful insights into reference material selection and in assessing the propagation of analytical error through normalisation procedures in stable oxygen isotope studies.

Identification of the seasonal conditions required for dormancy break of Persoonia longifolia (Proteaceae), a species with a woody indehiscent endocarp

BACKGROUND AND AIMS The mechanisms involved in breaking seed dormancy in species with woody endocarps are poorly understood. In a landmark study examining the role of endocarps in regulating germination, our aim was to investigate the effects of the natural sequence of environmental conditions on dormancy break of a species with a woody endocarp (Persoonia longifolia). METHODS The role of the endocarp in germination was investigated through imbibition and endocarp removal germination tests. The use of burial to break dormancy was examined and results from these experiments were used to guide laboratory investigations into the use of wet/dry cycling and stratification to break dormancy. KEY RESULTS Endocarps were water-permeable. Germination increased from 0 to 92·5 % when endocarps were removed. During burial in the field and nursery, 41·6 and 63·7 % of the endocarps germinated, respectively, after 36 months. Ex situ post-burial germination was cyclical and highest after 30 months of burial (45·4 % nursery and 31·8 % field). Highest germination occurred in wet/dry trials when the dry summer was long (20 weeks), had fluctuating temperatures (30/50 °C) and two long (7 d) wet cycles and was followed by moist winters at 10/20 °C. A stratification trial found that highest germination occurred following incubation for 12 weeks at 30 °C (including 2 weeks moist) + 6 weeks moist at 8 °C then placement at 20/10 °C for germination. CONCLUSIONS Summer conditions break physiological dormancy of the embryo and promote opening of the endocarp, allowing seeds to germinate during winter conditions. By closely monitoring the environment that endocarps are exposed to in nature, dormancy breaking mechanisms can be identified and used to improve germination. These results outline for the first time how dormancy and germination are regulated in a species with a hard woody endocarp, insights which will significantly improve our understanding of other species with similar reproductive features.

Developmental phenology of Persoonia longifolia (Proteaceae) and the impact of fire on these events

Persoonia longifolia R.Br. is a common understorey tree that is difficult to re-establish following bauxite extraction and land restoration in parts of the jarrah forest of south-western Western Australia. To improve restoration outcomes for P. longifolia, understanding its phenology is vital for developing methods for returning this plant to rehabilitated areas. The present study investigated in detail different aspects of the phenology of P. longifolia over a 3-year-period. Most vegetative growth occurred during the summer months and flowering and fruiting occurred concurrently. Fruit matured from July through to September, at which time these dropped to the forest floor. Germination occurred in late winter–early spring from fruit that was at least 1-year old, with poor seedling survival in natural bush (<10%) during the first 12 months. Following fire, P. longifolia plants resprouted prolifically in the next growing season, although there was very little fruit production in the first year following fire. Fruit was not produced until at least the second year following a fire, and when dispersed, required at least another year in the soil seed bank before germination commenced (i.e. 3 years post-fire). Results from the present study will improve restoration outcomes for this species, by providing guidance on better seed-collection strategies and baseline information concerning growth rates under natural conditions that can then be used to assess performance of this species in restored environments.

Managing evolving insecticide resistance in stored grain pests within Avon Region, Western Australia

The requirement that all Australian grain exports are insect-free relies on phosphine fumigation to control stored grain pests such as the lesser grain borer (Rhyzopertha dominica).The value of the current infrastructure in the grain storage, transport and handling network depends on the efficacy of phosphine fumigation. However, the biosecurity of the grain supply network is threatened by the emergence of Strong-Form Resistance (SFR) to phosphine in stored grain pests. SFR spreads from farm to farm through dispersion and by transport with grain through the supply network. The presence of SFR increases costs in the supply network as the number of fumigations per grain batch increases and the requirement to hold grain in sealed storage to achieve effective pest control increases. SFR in the short run increases costs of phosphine fumigation. In the long term it requires additional investment in sealed storage. This paper analyses the additional costs of SFR in the Western Australian Avon Region using three linked economic and bioeconomic models. Model 1 (farm to receival site) maximizes farmer’s profit of delivering wheat from farms to receival sites. Model 2 (recieval site to port) minimizes the Co-operative Bulk Handler’s (CBH) costs of transport, handling, storage and fumigation costs from receival sites to port. Model 3 (biosecurity – resistance spread) calculates the expected extra biosecurity cost of emerging SFR for the Avon wheat network. Our results show an increase in costs of between

Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series

8.8/t to

Long-term impacts of prescribed burning on regional extent and incidence of wildfires—Evidence from 50 years of active fire management in SW Australian forests

31.4/t of wheat depending on the rate of spread and the time horizon considered. Key words: Phosphine fumigation, biosecurity, crop storage, wheat, stored-wheat outbreak, jump diffusion.

Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index

Machine learning algorithms (MLAs) are often applied to identify Land Use and Land Cover (LULC) changes, but typically to only a limited set of imagery. This leaves the consistency of MLAs performance through time poorly understood. The research objective was therefore to compare the performance of six MLAs across a time-series of Landsat imagery (1979, 1992, 2003, 2014), all processed in the same manner. Here Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forests (RF), Learning Vector Quantization (LVQ), Recursive Partitioning, Regression Trees (RPART) and Stochastic Gradient Boosting (GBM) were evaluated. The results demonstrated that SVM achieved higher overall accuracies and kappa coefficients, and a slightly improved fit at individual class level, than the second best classifier RF. Both classifiers clearly outperformed the other algorithms. These results suggest that SVMs (or RFs) should be prioritised when classifying time-series imagery for LULC change detection.