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.

The use of genetic correlations to evaluate associations between SNP markers and quantitative traits

Open-pollinated progeny of Corymbia citriodora established in replicated field trials were assessed for stem diameter, wood density, and pulp yield prior to genotyping single nucleotide polymorphisms (SNP) and testing the significance of associations between markers and assessment traits. Multiple individuals within each family were genotyped and phenotyped, which facilitated a comparison of standard association testing methods and an alternative method developed to relate markers to additive genetic effects. Narrow-sense heritability estimates indicated there was significant additive genetic variance within this population for assessment traits ( h ˆ 2 =0.28to0.44 ) and genetic correlations between the three traits were negligible to moderate (r G = 0.08 to 0.50). The significance of association tests (p values) were compared for four different analyses based on two different approaches: (1) two software packages were used to fit standard univariate mixed models that include SNP-fixed effects, (2) bivariate and multivariate mixed models including each SNP as an additional selection trait were used. Within either the univariate or multivariate approach, correlations between the tests of significance approached +1; however, correspondence between the two approaches was less strong, although between-approach correlations remained significantly positive. Similar SNP markers would be selected using multivariate analyses and standard marker-trait association methods, where the former facilitates integration into the existing genetic analysis systems of applied breeding programs and may be used with either single markers or indices of markers created with genomic selection processes.

Population Expansion and Genetic Structure in Carcharhinus brevipinna in the Southern Indo-Pacific

Background:Quantifying genetic diversity and metapopulation structure provides insights into the evolutionary history of a species and helps develop appropriate management strategies. We provide the first assessment of genetic structure in spinner sharks (Carcharhinus brevipinna), a large cosmopolitan carcharhinid, sampled from eastern and northern Australia and South Africa. Methods and Findings:Sequencing of the mitochondrial DNA NADH dehydrogenase subunit 4 gene for 430 individuals revealed 37 haplotypes and moderately high haplotype diversity (h = 0.6770 ±0.025). While two metrics of genetic divergence (ΦST and FST) revealed somewhat different results, subdivision was detected between South Africa and all Australian locations (pairwise ΦST, range 0.02717–0.03508, p values ≤ 0.0013; pairwise FST South Africa vs New South Wales = 0.04056, p = 0.0008). Evidence for fine-scale genetic structuring was also detected along Australia’s east coast (pairwise ΦST = 0.01328, p < 0.015), and between south-eastern and northern locations (pairwise ΦST = 0.00669, p < 0.04).Conclusions: The Indian Ocean represents a robust barrier to contemporary gene flow in C. brevipinna between Australia and South Africa. Gene flow also appears restricted along a continuous continental margin in this species, with data tentatively suggesting the delineation of two management units within Australian waters. Further sampling, however, is required for a more robust evaluation of the latter finding. Evidence indicates that all sampled populations were shaped by a substantial demographic expansion event, with the resultant high genetic diversity being cause for optimism when considering conservation of this commercially-targeted species in the southern Indo-Pacific.

An overview of land application of pig effluent-P using soil P chemistry and mass balance calculations.

Attention is directed at land application of piggery effluent (containing urine, faeces, water, and wasted feed) as a potential source of water resource contamination with phosphorus (P). This paper summarises P-related properties of soil from 0-0.05 m depth at 11 piggery effluent application sites, in order to explore the impact that effluent application has had on the potential for run-off transport of P. The sites investigated were situated on Alfisol, Mollisol, Vertisol, and Spodosol soils in areas that received effluent for 1.5-30 years (estimated effluent-P applications of 100-310000 kg P/ha in total). Total (PT), bicarbonate extractable (PB), and soluble P forms were determined for the soil (0-0.05 m) at paired effluent and no-effluent sites, as well as texture, oxalate-extractable Fe and Al, organic carbon, and pH. All forms of soil P at 0-0.05 m depth increased with effluent application (PB at effluent sites was 1.7-15 times that at no-effluent sites) at 10 of the 11 sites. Increases in PB were strongly related to net P applications (regression analysis of log values for 7 sites with complete data sets: 82.6 % of variance accounted for, p <0.01). Effluent irrigation tended to increase the proportion of soil PT in dilute CaCl2-extractable forms (PTC: effluent average 2.0 %; no-effluent average 0.6%). The proportion of PTC in non-molybdate reactive forms (centrifuged supernatant) decreased (no-effluent average, 46.4 %; effluent average, 13.7 %). Anaerobic lagoon effluent did not reliably acidify soil, since no consistent relationship was observed for pH with effluent application. Soil organic carbon was increased in most of the effluent areas relative to the no-effluent areas. The four effluent areas where organic carbon was reduced had undergone intensive cultivation and cropping. Current effluent management at many of the piggeries failed to maximise the potential for waste P recapture. Ten of the case-study effluent application areas have received effluent-P in excess of crop uptake. While this may not represent a significant risk of leaching where sorption retains P, it has increased the risk of transport of P by run-off. Where such sites are close to surface water, run-off P loads should be managed.

Plasma biochemistry and hematologic values for wild-caught flying foxes (Pteropus giganteus) in India

Although bats of the genus Pteropus are important ecologically as pollinators and natural hosts for zoonotic pathogens, little is known about their basic physiology. Hematology and plasma biochemistries were determined from wild-caught flying foxes (Pteropus giganteus) in northern India (n = 41). Mean lymphocyte differential count was higher for juveniles than adults. Mean platelet count was lower than previously reported. No hemoparasites were observed. No differences were observed between plasma biochemistry values of male and female bats, juveniles and adults, or lactating and nonlactating females. Variation in aspartate aminotransferase (AST) was seen based on body condition score. Blood urea nitrogen and cholesterol concentrations were lower in P. giganteus than other mammalian groups, but were consistent with those reported from other Pteropus species. Alanine aminotransferase and AST concentrations were higher than those reported for Pteropus vampyrus, a closely related species. This study provides basic physiologic information that can be used in future health and disease studies of Indian flying foxes.

Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing

This paper describes the development of a model, based on Bayesian networks, to estimate the likelihood that sheep flocks are infested with lice at shearing and to assist farm managers or advisers to assess whether or not to apply a lousicide treatment. The risk of lice comes from three main sources: (i) lice may have been present at the previous shearing and not eradicated; (ii) lice may have been introduced with purchased sheep; and (iii) lice may have entered with strays. A Bayesian network is used to assess the probability of each of these events independently and combine them for an overall assessment. Rubbing is a common indicator of lice but there are other causes too. If rubbing has been observed, an additional Bayesian network is used to assess the probability that lice are the cause. The presence or absence of rubbing and its possible cause are combined with these networks to improve the overall risk assessment.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.