Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

Wild dogma II: the role and implications of wild dogma for wild dog management in Australia

The studies of Allen (2011) and Allen et al. (2011) recently examined the methodology underpinning claims that dingoes provide net benefits to biodiversity by suppressing foxes and cats. They found most studies to have design flaws and/or observational methods that preclude valid interpretations from the data, describing most of the current literature as ‘wild dogma’. In this short supplement, we briefly highlight the roles and implications of wild dogma for wild dog management in Australia. We discuss nomenclature, and the influence that unreliable science can have on policy and practice changes related to apex predator management.

Isolation and characterization of 21 polymorphic microsatellite loci in the iconic Australian lungfish, Neoceratodus forsteri, using the Ion Torrent next-generation sequencing platform

We isolated and characterized 21 microsatellite loci in the vulnerable and iconic Australian lungfish, Neoceratodus forsteri. Loci were screened across eight individuals from the Burnett River and 40 individuals from the Pine River. Genetic diversity was low with between one and six alleles per locus within populations and a maximum expected heterozygosity of 0.774. These loci will now be available to assess effective population sizes and genetic structure in N. forsteri across its natural range in South East Queensland, Australia.

IPM in the transgenic era: a review of the challenges from emerging pests in Australian cotton systems

The Cotton Catchment Communities Cooperative Research Centre began during a period of rapid uptake of Bollgard II® cotton, which contains genes to express two Bt proteins that control the primary pests of cotton in Australia, Helicoverpa armigera and H. punctigera. The dramatic uptake of this technology presumably resulted in strong selection pressure for resistance in Helicoverpa spp. against the Bt proteins. The discovery of higher than expected levels of resistance in both species against one of the proteins in Bollgard II® cotton (Cry2Ab) led to significant re-evaluation of the resistance management plan developed for this technology, which was a core area of research for the Cotton CRC. The uptake of Bollgard II® cotton also led to a substantial decline in pesticide applications against Helicoverpa spp. (from 10–14 to 0–3 applications per season). The low spray environment allowed some pests not controlled by the Bt proteins to emerge as more significant pests, especially sucking species such as Creontiades dilutus and Nezara viridula. A range of other minor pests have also sporadically arisen as problems. Lack of knowledge and experience with these pests created uncertainty and encouraged insecticide use, which threatened to undermine the gains made with Bollgard II® cotton. Here we chronicle the achievements of the Cotton CRC in providing the industry with new knowledge and management strategies for these pests.